Hs_Apr3
Human fetal trachea samples collected on Apr3 2019. 10X genomics 3’ kit, v3 chemistry.
The samples come from 2 individuals of gestational ages of 21 weeks and 23 weeks respectively.
library(Seurat)
library(dplyr)ZipF<-list.files(path=".",pattern="*.gz",full.names = T,recursive = T)
ZipFlibrary(plyr)
library(R.utils)
ldply(.data=ZipF, .fun=gunzip) #This just unzips locally##### First I manually changed all featurres.tsv to genes.tsv. Otherwise Read10X (Seurat v2) would not recognize.
# Load data
file_10Xdir_Hs<-c("GA21wk_v3","GA23wk_v3")
names(file_10Xdir_Hs)<-c("GA21wk_v3","GA23wk_v3")
Hs_Apr3_v3.data <- Read10X(data.dir = file_10Xdir_Hs)dim(Hs_Apr3_v3.data)26577 genes based on HG38-plus reference
38892 “cells” are identified by Cell Ranger
Hs_GA2123_Trachea_v3 <- CreateSeuratObject(raw.data = Hs_Apr3_v3.data, min.cells = 1, min.genes = 1,
project = "Hs_GA2123_Trachea_v3chemistry")
Hs_GA2123_Trachea_v3@raw.data@Dimhead(Hs_GA2123_Trachea_v3@cell.names)Hs_GA2123_Trachea_v3 <- FilterCells(object = Hs_GA2123_Trachea_v3, subset.names = c("nGene","nUMI"),
low.thresholds = c(1000,4000), high.thresholds = c(Inf,Inf))
Hs_GA2123_Trachea_v3@data@Dimcell_name<-read.table(text=Hs_GA2123_Trachea_v3@cell.names,sep="_",colClasses = "character")
age<-cell_name[,1]
names(age)<-Hs_GA2123_Trachea_v3@cell.namesHs_GA2123_Trachea_v3<-AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = age, col.name = "age")table(Hs_GA2123_Trachea_v3@meta.data$age)ribo.genes <- grep(pattern = "^RP[SL][[:digit:]]", x = rownames(x = Hs_GA2123_Trachea_v3@data), value = TRUE)
percent.ribo <- Matrix::colSums(Hs_GA2123_Trachea_v3@raw.data[ribo.genes, ])/Matrix::colSums(Hs_GA2123_Trachea_v3@raw.data)
Hs_GA2123_Trachea_v3 <- AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = percent.ribo, col.name = "percent.ribo")aggregate(Hs_GA2123_Trachea_v3@meta.data[, c(1:2,5)], list(Hs_GA2123_Trachea_v3@meta.data$age), median)
Hs_GA2123_Trachea_v3 <- NormalizeData(object = Hs_GA2123_Trachea_v3)Hs_GA2123_Trachea_v3 <- ScaleData(object = Hs_GA2123_Trachea_v3)Hs_GA2123_Trachea_v3 <- FindVariableGenes(object = Hs_GA2123_Trachea_v3, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)Hs_GA2123_Trachea_v3 <- RunPCA(object = Hs_GA2123_Trachea_v3, do.print = FALSE)
Hs_GA2123_Trachea_v3 <- ProjectPCA(object = Hs_GA2123_Trachea_v3, do.print = FALSE)PCHeatmap(object = Hs_GA2123_Trachea_v3, pc.use = 1:10, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)
PCElbowPlot(object = Hs_GA2123_Trachea_v3)n.pcs = 20
res.used <- 0.8
Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2)
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.0.8",pt.size = 0.2)
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = F,group.by="age",pt.size = 0.1)n.pcs = 20
res.used <- 1.0
Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE,force.recalc=T)Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1")
DoHeatmap(object = Hs_GA2123_Trachea_v3, genes.use = c("ANO1","CFTR","EPCAM","TP63","FOXJ1","FOXN4","SCGB1A1","LTF","SNAP25","ASCL1","CHGA","PLP1","MPZ","SOX10","C1QA","FCER1G","PECAM1","LYVE1","RGS5","NOTCH3","ACTA2","ACTG2","DES","PDLIM3","FGL2","PCDH7","MYH11","COL11A1","SOX9","SOX5","SOX6","COL2A1","ACAN","SERPINF1","COL1A1","THBS2","KERA","DCN","LUM","CD34","WNT2","THY1","PI16","CLEC3B","MKI67","TOP2A","TWIST2"),
slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1",group.cex = 25,cex.row=25,group.order = c(9,16,12,14,10,19,3,22,20,21,5,7,15,17,8,18,4,0,2,1,13,11,6)
)Hs_GA2123_Trachea_v3<-SetAllIdent(Hs_GA2123_Trachea_v3,id="res.1")
GA2123wk_v3.res1.clust.markers <- FindAllMarkers(object = Hs_GA2123_Trachea_v3, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
GA2123wk_v3.res1.clust.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)write.table(GA2123wk_v3.res1.clust.markers,"GA2123wk_v3.res1.markers.txt",sep="\t")
Hs_v3_res1_8_18<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(8),ident.2=c(18),only.pos = F)
Hs_v3_res1_8_18Hs_v3_res1_2over1<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(2),ident.2=c(1),only.pos = T)
Hs_v3_res1_2over1Hs_v3_res1_21_20<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(21),ident.2=c(20),only.pos = T)
Hs_v3_res1_21_20n.pcs = 20
res.used <- 1.2
Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1.2")
n.pcs = 20
res.used <- 1.4
Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1.4")
inport doublet scores generated by Scrublet, and make them a metadata column:
load("GA2123wk_apr3_v3_doubletScore.RData")
Hs_GA2123_Trachea_v3<-AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = GA2123wk_apr3_v3_doubletScore, col.name = "doublet_score")
sum(is.na(Hs_GA2123_Trachea_v3@meta.data$doublet_score))Hs_GA2123_Trachea_v3<-SetAllIdent(Hs_GA2123_Trachea_v3,id="age")
VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("doublet_score"), nCol = 1,group.by="res.1.4",point.size.use=0.3,ident.include = "GA21wk")VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("doublet_score"), nCol = 1,group.by="res.1.4",point.size.use=0.3,ident.include = "GA23wk")DoHeatmap(object = Hs_GA2123_Trachea_v3, genes.use = c("ANO1","CFTR","EPCAM","KRT8","KRT18","TP63","KRT5","KRT14","SOSTDC1","KRT4","KRT13","SPDEF","CREB3L1","MUC5B","FOXJ1","FOXN4","SHISA8","MCIDAS","TUBB3","SNAP25","ASCL1","CHGA","PLP1","MPZ","C1QA","FCER1G","CD3G","PECAM1","NRP1","LYVE1","RGS5","NOTCH3","ACTA2","TAGLN","MYH11","COL8A1","COL11A1","SOX9","COL2A1","ACAN","MIA","DCN","LUM","CD34","WNT2","THY1","PI16","CLEC3B","TK1","MKI67","TOP2A","ALAS2"),
slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 35,cex.row=25,group.order = c(8,14,17,19,10,21,9,11,24,22,23,3,5,16,18,6,20,13,7,0,1,2,15,12,4)
)
VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("SOX10","PHOX2A", "PHOX2B","CHGA","ASCL1","RET"), nCol = 1,group.by="res.1.4",point.size.use=0.3) DoHeatmap(object = Hs_GA2123_Trachea_v3, genes.use = c("EPCAM","TUBB3","SNAP25","ASCL1","CHGA","PHOX2A","PHOX2B","PLP1","MPZ"),
slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 35,cex.row=25,cells.use = Hs_GA2123_Trachea_v3@cell.names[Hs_GA2123_Trachea_v3@meta.data$res.1.4 %in% c(10)]
)subset the Non-EPCAM cells:
Hs_GA2123_Trachea_v3 <- SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "res.1.4")
Hs_GA2123_Trachea_v3_nonEpcam<-SubsetData(object=Hs_GA2123_Trachea_v3,ident.use=c(0:7,9:13,15,16,18,20:24))
table(Hs_GA2123_Trachea_v3_nonEpcam@meta.data$res.1.4)colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.0.8'] <- 'orig.0.8'
colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.1.4'] <- 'orig.1.4'
colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.1.2'] <- 'orig.1.2'Hs_GA2123_Trachea_v3_nonEpcam <- ScaleData(object = Hs_GA2123_Trachea_v3_nonEpcam)Hs_GA2123_Trachea_v3_nonEpcam <- FindVariableGenes(object = Hs_GA2123_Trachea_v3_nonEpcam, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)run PCA on the set of genes
Hs_GA2123_Trachea_v3_nonEpcam <- RunPCA(object = Hs_GA2123_Trachea_v3_nonEpcam, do.print = FALSE)
#PCAPlot(Hs_GA2123_Trachea_v3_nonEpcam)Hs_GA2123_Trachea_v3_nonEpcam <- ProjectPCA(object = Hs_GA2123_Trachea_v3_nonEpcam, do.print = F)PCElbowPlot(object = Hs_GA2123_Trachea_v3_nonEpcam)PCHeatmap(object = Hs_GA2123_Trachea_v3_nonEpcam, pc.use = 1:20, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)
n.pcs = 20
res.used <- 0.8
Hs_GA2123_Trachea_v3_nonEpcam <- FindClusters(object = Hs_GA2123_Trachea_v3_nonEpcam, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE,force.recalc = T)Hs_GA2123_Trachea_v3_nonEpcam <- RunTSNE(object = Hs_GA2123_Trachea_v3_nonEpcam, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = Hs_GA2123_Trachea_v3_nonEpcam, do.label = T,group.by="res.0.8")
DoHeatmap(object = Hs_GA2123_Trachea_v3_nonEpcam, genes.use = c("ANO1","CFTR","TUBB3","SNAP25","ASCL1","CHGA","PLP1","MPZ","C1QA","FCER1G","CD3G","PECAM1","NRP1","LYVE1","RGS5","NOTCH3","ACTA2","TAGLN","MYH11","COL8A1","COL11A1","SOX9","COL2A1","ACAN","MIA","DCN","LUM","CD34","WNT2","THY1","PI16","CLEC3B","TK1","MKI67","TOP2A","ADIPOQ","CAR3"),
slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.0.8",group.cex = 35,cex.row=25,group.order = c(9,14,2,17,15,16,13,6,8,5,4,0,3,1,10,11,12,7)
)
table(Hs_GA2123_Trachea_v3_nonEpcam@meta.data$orig.1.4,Hs_GA2123_Trachea_v3_nonEpcam@meta.data$res.0.8)library(ggalluvial)ggplot(data=Hs_GA2123_Trachea_v3_nonEpcam@meta.data,aes(axis1=orig.1.4,axis2=res.0.8))+geom_alluvium(aes(fill=res.0.8))+geom_stratum(width = 1/12, fill = "black", color = "grey") +geom_label(stat = "stratum", label.strata = TRUE)+scale_x_discrete(limits = c("orig.1.4", "res.0.8"), expand = c(.05, .05))Now subset the basal, ciliated, and secretory:
Hs_GA2123_Trachea_v3 <- SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "res.1.4")
Hs_GA2123_Trachea_v3_sub1<-SubsetData(object=Hs_GA2123_Trachea_v3,ident.use=c(8,14,19))
table(Hs_GA2123_Trachea_v3_sub1@meta.data$res.1.4)colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.0.8'] <- 'orig.0.8'
colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.1.4'] <- 'orig.1.4'
colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.1.2'] <- 'orig.1.2'Hs_GA2123_Trachea_v3_sub1 <- ScaleData(object = Hs_GA2123_Trachea_v3_sub1)Hs_GA2123_Trachea_v3_sub1 <- FindVariableGenes(object = Hs_GA2123_Trachea_v3_sub1, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)run PCA on the set of genes
Hs_GA2123_Trachea_v3_sub1 <- RunPCA(object = Hs_GA2123_Trachea_v3_sub1, do.print = FALSE)
#PCAPlot(Hs_GA2123_Trachea_v3_sub1)Hs_GA2123_Trachea_v3_sub1 <- ProjectPCA(object = Hs_GA2123_Trachea_v3_sub1, do.print = F)PCElbowPlot(object = Hs_GA2123_Trachea_v3_sub1)PCHeatmap(object = Hs_GA2123_Trachea_v3_sub1, pc.use = 1:12, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)
n.pcs = 16
res.used <- 0.8
Hs_GA2123_Trachea_v3_sub1 <- FindClusters(object = Hs_GA2123_Trachea_v3_sub1, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)Hs_GA2123_Trachea_v3_sub1 <- RunTSNE(object = Hs_GA2123_Trachea_v3_sub1, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = Hs_GA2123_Trachea_v3_sub1, do.label = T)
DoHeatmap(object = Hs_GA2123_Trachea_v3_sub1, genes.use = c("TP63","KRT15","KRT5","KRT17","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","KRT4","MUC1","MUC4","MUC20","SERPINB3","GSTP1","ALOX15","CD9","MYH11","ACTG2","MYLK","TAGLN","LTF","AZGP1","DMBT1","FCGBP","CCL28","AQP5","MUC5B","SPDEF","RNASE1","LYZ","TIMP3","OGN","COL14A1","BGN","COL11A1","LUM","ACAN","CFTR","ANO1","TACSTD2"),
slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.0.8",group.cex = 60,cex.row=30,group.order = c(4,2,6,1,7,5,0,3)
)
DotPlot(object = Hs_GA2123_Trachea_v3_sub1, cols.use = c("forestgreen","magenta3"),genes.plot = c("TP63","KRT15","KRT5","KRT17","KRT14","SOSTDC1","FOXJ1","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","CFAP53","CETN2","KRT4","KRT13","MUC1","MUC4","MUC16","MUC20","SERPINB3","MYH11","ACTG2","MYLK","APOE","TAGLN","LTF","AZGP1","DMBT1","KCNN4","FCGBP","LRRC26","KRT7","CCL28","AQP5","MUC5B","SPDEF","LYZ","TIMP3","OGN","COL14A1","BGN","MGP","COL11A1","LUM","ACAN","CFTR","ANO1"),group.by = "ident", x.lab.rot = T,plot.legend = T)prop.table(table(Hs_GA2123_Trachea_v3_sub1@meta.data$age,Hs_GA2123_Trachea_v3_sub1@meta.data$res.0.8),1)
n.pcs = 16
res.used <- 1.2
Hs_GA2123_Trachea_v3_sub1 <- FindClusters(object = Hs_GA2123_Trachea_v3_sub1, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)Hs_GA2123_Trachea_v3_sub1 <- RunTSNE(object = Hs_GA2123_Trachea_v3_sub1, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = Hs_GA2123_Trachea_v3_sub1, do.label = T,group.by="res.1.2")
DoHeatmap(object = Hs_GA2123_Trachea_v3_sub1, genes.use = c("TP63","KRT15","KRT5","KRT17","KRT14","SOSTDC1","SMOC2","SOX9","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","KRT4","MUC1","MUC4","MUC20","SERPINB3","GSTP1","ALOX15","CD9","MYH11","ACTG2","MYLK","TAGLN","LTF","AZGP1","DMBT1","FCGBP","CCL28","AQP5","MUC5B","SPDEF","RNASE1","LYZ","TIMP3","OGN","COL14A1","BGN","COL11A1","LUM","ACAN","CFTR","ANO1","TACSTD2","SERPINB4","SERPINB13","NPPC"),
slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.2",group.cex = 60,cex.row=30,group.order = c(2,4,6,1,7,8,5,0,3)
)
Hs_v3_sub1_res1.2_c8over2_4<-FindMarkers(Hs_GA2123_Trachea_v3_sub1,ident.1=c(8),ident.2 = c(2,4),only.pos = TRUE)
Hs_v3_sub1_res1.2_c8over2_4library(plyr)
Hs_GA2123_Trachea_v3_sub1@meta.data$cell_type<-mapvalues(Hs_GA2123_Trachea_v3_sub1@meta.data$res.1.2,from=c("0","1","2","3","4","5","6","7","8"),to=c("Secretory_SMG","Ciliated","Basal_SE","Epcam_ECM","Basal_SE","Myoepithelial","Ciliated_Foxn4","Secretory_SE","Basal_SMG"))DoHeatmap(object = Hs_GA2123_Trachea_v3_sub1, genes.use = c("FOXN4","PLK4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","TP63","KRT15","KRT5","KRT17","KRT14","SOSTDC1","SMOC2","NPPC","KRT4","MUC1","MUC4","MUC20","SERPINB3","SERPINB4","SERPINB13","GSTP1","ALOX15","CD9","SOX9","LTF","AQP5","LRRC26","AZGP1","DMBT1","FCGBP","CCL28","MUC5B","SPDEF","RNASE1","LYZ","MYH11","ACTG2","MYLK","TAGLN","TIMP3","OGN","COL14A1","BGN","COL11A1","LUM","ACAN","CFTR","ANO1"),
slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="cell_type",group.cex = 60,cex.row=30,group.order = c("Ciliated_Foxn4","Ciliated","Basal_SE","Secretory_SE","Basal_SMG","Secretory_SMG","Myoepithelial","Epcam_ECM")
)
To annotate Hs_GA2123_Trachea_v3:
Hs_v3_type_sub1<-Hs_GA2123_Trachea_v3_sub1@meta.data$cell_type
names(Hs_v3_type_sub1)<-Hs_GA2123_Trachea_v3_sub1@cell.namesHs_GA2123_Trachea_v3@meta.data$cell_type<-mapvalues(Hs_GA2123_Trachea_v3@meta.data$res.1,from=c("0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22"),to=c("Fibroblast","Fibroblast","Fibroblast","VascularEndothelial","Fibroblast","Fibroblast","CyclingFibroblast","Fibroblast","Chondrocyte","Basal","Schwann/Neural","Fibroblast","Secretory","MesenchymalProgenitor","Stem","Fibroblast","Ciliated","Fibroblast","Chondrocyte","Immune","Muscle","Muscle","LymphaticEndothelial"))use the Hs_GA2123_Trachea_v3_sub1 information to further annotate Hs_GA2123_Trachea_v3:
Hs_GA2123_Trachea_v3<-AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = Hs_v3_type_sub1, col.name = "specific_type")table(Hs_GA2123_Trachea_v3@meta.data$specific_type)Hs_GA2123_Trachea_v3@meta.data$specific_type <- ifelse(is.na(Hs_GA2123_Trachea_v3@meta.data$specific_type), as.character(Hs_GA2123_Trachea_v3@meta.data$cell_type), as.character(Hs_GA2123_Trachea_v3@meta.data$specific_type))now we have annotation for all cells:
table(Hs_GA2123_Trachea_v3@meta.data$specific_type,Hs_GA2123_Trachea_v3@meta.data$age)
GA21wk GA23wk
Basal_SE 180 128
Basal_SMG 12 18
Chondrocyte 629 21
Ciliated 133 36
Ciliated_Foxn4 41 25
CyclingFibroblast 393 86
Epcam_ECM 112 38
Fibroblast 4586 773
Immune 103 18
LymphaticEndothelial 48 17
MesenchymalProgenitor 302 14
Muscle 112 65
Myoepithelial 35 32
Schwann/Neural 316 89
Secretory_SE 32 22
Secretory_SMG 115 61
Stem 11 275
VascularEndothelial 463 352Hs_GA2123_Trachea_v3<-SetAllIdent(Hs_GA2123_Trachea_v3,id="specific_type")
GA2123wk_v3.specific.type.markers <- FindAllMarkers(object = Hs_GA2123_Trachea_v3, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
| | 0 % ~calculating
|+ | 1 % ~03m 26s
|++ | 2 % ~03m 25s
|++ | 3 % ~03m 27s
|+++ | 4 % ~03m 22s
|+++ | 5 % ~03m 20s
|++++ | 6 % ~03m 18s
|++++ | 7 % ~03m 22s
|+++++ | 8 % ~03m 19s
|+++++ | 9 % ~03m 15s
|++++++ | 10% ~03m 13s
|++++++ | 11% ~03m 10s
|+++++++ | 12% ~03m 10s
|+++++++ | 13% ~03m 07s
|++++++++ | 14% ~03m 05s
|++++++++ | 15% ~03m 02s
|+++++++++ | 16% ~02m 59s
|+++++++++ | 17% ~02m 57s
|++++++++++ | 18% ~02m 55s
|++++++++++ | 19% ~02m 56s
|+++++++++++ | 20% ~02m 53s
|+++++++++++ | 21% ~02m 50s
|++++++++++++ | 22% ~02m 48s
|++++++++++++ | 23% ~02m 46s
|+++++++++++++ | 24% ~02m 43s
|+++++++++++++ | 25% ~02m 41s
|++++++++++++++ | 26% ~02m 39s
|++++++++++++++ | 27% ~02m 37s
|+++++++++++++++ | 28% ~02m 35s
|+++++++++++++++ | 29% ~02m 33s
|++++++++++++++++ | 30% ~02m 31s
|++++++++++++++++ | 31% ~02m 28s
|+++++++++++++++++ | 32% ~02m 27s
|+++++++++++++++++ | 33% ~02m 25s
|++++++++++++++++++ | 34% ~02m 22s
|++++++++++++++++++ | 35% ~02m 20s
|+++++++++++++++++++ | 36% ~02m 20s
|+++++++++++++++++++ | 37% ~02m 18s
|++++++++++++++++++++ | 38% ~02m 15s
|++++++++++++++++++++ | 39% ~02m 13s
|+++++++++++++++++++++ | 40% ~02m 10s
|+++++++++++++++++++++ | 41% ~02m 08s
|++++++++++++++++++++++ | 42% ~02m 05s
|++++++++++++++++++++++ | 43% ~02m 03s
|+++++++++++++++++++++++ | 44% ~02m 01s
|+++++++++++++++++++++++ | 45% ~01m 58s
|++++++++++++++++++++++++ | 46% ~01m 56s
|++++++++++++++++++++++++ | 47% ~01m 54s
|+++++++++++++++++++++++++ | 48% ~01m 52s
|+++++++++++++++++++++++++ | 49% ~01m 49s
|++++++++++++++++++++++++++ | 51% ~01m 47s
|++++++++++++++++++++++++++ | 52% ~01m 45s
|+++++++++++++++++++++++++++ | 53% ~01m 43s
|+++++++++++++++++++++++++++ | 54% ~01m 40s
|++++++++++++++++++++++++++++ | 55% ~01m 39s
|++++++++++++++++++++++++++++ | 56% ~01m 36s
|+++++++++++++++++++++++++++++ | 57% ~01m 34s
|+++++++++++++++++++++++++++++ | 58% ~01m 32s
|++++++++++++++++++++++++++++++ | 59% ~01m 30s
|++++++++++++++++++++++++++++++ | 60% ~01m 28s
|+++++++++++++++++++++++++++++++ | 61% ~01m 25s
|+++++++++++++++++++++++++++++++ | 62% ~01m 23s
|++++++++++++++++++++++++++++++++ | 63% ~01m 21s
|++++++++++++++++++++++++++++++++ | 64% ~01m 19s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 17s
|+++++++++++++++++++++++++++++++++ | 66% ~01m 14s
|++++++++++++++++++++++++++++++++++ | 67% ~01m 12s
|++++++++++++++++++++++++++++++++++ | 68% ~01m 10s
|+++++++++++++++++++++++++++++++++++ | 69% ~01m 08s
|+++++++++++++++++++++++++++++++++++ | 70% ~01m 06s
|++++++++++++++++++++++++++++++++++++ | 71% ~01m 03s
|++++++++++++++++++++++++++++++++++++ | 72% ~01m 01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~59s
|+++++++++++++++++++++++++++++++++++++ | 74% ~57s
|++++++++++++++++++++++++++++++++++++++ | 75% ~55s
|++++++++++++++++++++++++++++++++++++++ | 76% ~53s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~51s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~49s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~46s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~44s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~42s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~40s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~37s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~35s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~33s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~31s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~29s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~26s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~24s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~20s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~18s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~16s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03m 38s
| | 0 % ~calculating
|+ | 1 % ~03m 19s
|++ | 2 % ~03m 17s
|++ | 3 % ~03m 14s
|+++ | 4 % ~03m 12s
|+++ | 5 % ~03m 10s
|++++ | 6 % ~03m 09s
|++++ | 7 % ~03m 08s
|+++++ | 8 % ~03m 05s
|+++++ | 9 % ~03m 03s
|++++++ | 10% ~03m 05s
|++++++ | 11% ~03m 02s
|+++++++ | 12% ~03m 00s
|+++++++ | 13% ~02m 58s
|++++++++ | 14% ~02m 56s
|++++++++ | 15% ~02m 53s
|+++++++++ | 16% ~02m 51s
|+++++++++ | 18% ~02m 49s
|++++++++++ | 19% ~02m 47s
|++++++++++ | 20% ~02m 45s
|+++++++++++ | 21% ~02m 43s
|+++++++++++ | 22% ~02m 41s
|++++++++++++ | 23% ~02m 39s
|++++++++++++ | 24% ~02m 38s
|+++++++++++++ | 25% ~02m 36s
|+++++++++++++ | 26% ~02m 34s
|++++++++++++++ | 27% ~02m 32s
|++++++++++++++ | 28% ~02m 30s
|+++++++++++++++ | 29% ~02m 27s
|+++++++++++++++ | 30% ~02m 25s
|++++++++++++++++ | 31% ~02m 23s
|++++++++++++++++ | 32% ~02m 21s
|+++++++++++++++++ | 33% ~02m 21s
|++++++++++++++++++ | 34% ~02m 19s
|++++++++++++++++++ | 35% ~02m 17s
|+++++++++++++++++++ | 36% ~02m 14s
|+++++++++++++++++++ | 37% ~02m 12s
|++++++++++++++++++++ | 38% ~02m 10s
|++++++++++++++++++++ | 39% ~02m 07s
|+++++++++++++++++++++ | 40% ~02m 05s
|+++++++++++++++++++++ | 41% ~02m 05s
|++++++++++++++++++++++ | 42% ~02m 03s
|++++++++++++++++++++++ | 43% ~02m 01s
|+++++++++++++++++++++++ | 44% ~01m 58s
|+++++++++++++++++++++++ | 45% ~01m 56s
|++++++++++++++++++++++++ | 46% ~01m 54s
|++++++++++++++++++++++++ | 47% ~01m 51s
|+++++++++++++++++++++++++ | 48% ~01m 49s
|+++++++++++++++++++++++++ | 49% ~01m 47s
|++++++++++++++++++++++++++ | 51% ~01m 44s
|++++++++++++++++++++++++++ | 52% ~01m 42s
|+++++++++++++++++++++++++++ | 53% ~01m 40s
|+++++++++++++++++++++++++++ | 54% ~01m 38s
|++++++++++++++++++++++++++++ | 55% ~01m 35s
|++++++++++++++++++++++++++++ | 56% ~01m 33s
|+++++++++++++++++++++++++++++ | 57% ~01m 31s
|+++++++++++++++++++++++++++++ | 58% ~01m 29s
|++++++++++++++++++++++++++++++ | 59% ~01m 26s
|++++++++++++++++++++++++++++++ | 60% ~01m 24s
|+++++++++++++++++++++++++++++++ | 61% ~01m 22s
|+++++++++++++++++++++++++++++++ | 62% ~01m 20s
|++++++++++++++++++++++++++++++++ | 63% ~01m 18s
|++++++++++++++++++++++++++++++++ | 64% ~01m 16s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 14s
|+++++++++++++++++++++++++++++++++ | 66% ~01m 11s
|++++++++++++++++++++++++++++++++++ | 67% ~01m 09s
|+++++++++++++++++++++++++++++++++++ | 68% ~01m 07s
|+++++++++++++++++++++++++++++++++++ | 69% ~01m 05s
|++++++++++++++++++++++++++++++++++++ | 70% ~01m 03s
|++++++++++++++++++++++++++++++++++++ | 71% ~01m 00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~58s
|+++++++++++++++++++++++++++++++++++++ | 73% ~56s
|++++++++++++++++++++++++++++++++++++++ | 74% ~54s
|++++++++++++++++++++++++++++++++++++++ | 75% ~52s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~50s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~48s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~45s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~43s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~41s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~39s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~37s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~35s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~32s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~30s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~28s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~26s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~24s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~19s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~17s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03m 32s
| | 0 % ~calculating
|+ | 1 % ~02m 37s
|++ | 2 % ~02m 37s
|++ | 3 % ~02m 34s
|+++ | 4 % ~02m 34s
|+++ | 5 % ~02m 32s
|++++ | 7 % ~02m 30s
|++++ | 8 % ~02m 28s
|+++++ | 9 % ~02m 27s
|+++++ | 10% ~02m 25s
|++++++ | 11% ~02m 24s
|++++++ | 12% ~02m 22s
|+++++++ | 13% ~02m 21s
|++++++++ | 14% ~02m 22s
|++++++++ | 15% ~02m 20s
|+++++++++ | 16% ~02m 18s
|+++++++++ | 17% ~02m 16s
|++++++++++ | 18% ~02m 15s
|++++++++++ | 20% ~02m 13s
|+++++++++++ | 21% ~02m 11s
|+++++++++++ | 22% ~02m 09s
|++++++++++++ | 23% ~02m 07s
|++++++++++++ | 24% ~02m 06s
|+++++++++++++ | 25% ~02m 04s
|++++++++++++++ | 26% ~02m 02s
|++++++++++++++ | 27% ~02m 01s
|+++++++++++++++ | 28% ~01m 59s
|+++++++++++++++ | 29% ~01m 57s
|++++++++++++++++ | 30% ~01m 55s
|++++++++++++++++ | 32% ~01m 55s
|+++++++++++++++++ | 33% ~01m 53s
|+++++++++++++++++ | 34% ~01m 51s
|++++++++++++++++++ | 35% ~01m 49s
|++++++++++++++++++ | 36% ~01m 47s
|+++++++++++++++++++ | 37% ~01m 46s
|++++++++++++++++++++ | 38% ~01m 44s
|++++++++++++++++++++ | 39% ~01m 42s
|+++++++++++++++++++++ | 40% ~01m 40s
|+++++++++++++++++++++ | 41% ~01m 40s
|++++++++++++++++++++++ | 42% ~01m 38s
|++++++++++++++++++++++ | 43% ~01m 36s
|+++++++++++++++++++++++ | 45% ~01m 34s
|+++++++++++++++++++++++ | 46% ~01m 32s
|++++++++++++++++++++++++ | 47% ~01m 31s
|++++++++++++++++++++++++ | 48% ~01m 29s
|+++++++++++++++++++++++++ | 49% ~01m 27s
|+++++++++++++++++++++++++ | 50% ~01m 27s
|++++++++++++++++++++++++++ | 51% ~01m 25s
|+++++++++++++++++++++++++++ | 52% ~01m 23s
|+++++++++++++++++++++++++++ | 53% ~01m 21s
|++++++++++++++++++++++++++++ | 54% ~01m 19s
|++++++++++++++++++++++++++++ | 55% ~01m 17s
|+++++++++++++++++++++++++++++ | 57% ~01m 15s
|+++++++++++++++++++++++++++++ | 58% ~01m 13s
|++++++++++++++++++++++++++++++ | 59% ~01m 11s
|++++++++++++++++++++++++++++++ | 60% ~01m 09s
|+++++++++++++++++++++++++++++++ | 61% ~01m 07s
|+++++++++++++++++++++++++++++++ | 62% ~01m 05s
|++++++++++++++++++++++++++++++++ | 63% ~01m 03s
|+++++++++++++++++++++++++++++++++ | 64% ~01m 01s
|+++++++++++++++++++++++++++++++++ | 65% ~59s
|++++++++++++++++++++++++++++++++++ | 66% ~58s
|++++++++++++++++++++++++++++++++++ | 67% ~56s
|+++++++++++++++++++++++++++++++++++ | 68% ~54s
|+++++++++++++++++++++++++++++++++++ | 70% ~52s
|++++++++++++++++++++++++++++++++++++ | 71% ~50s
|++++++++++++++++++++++++++++++++++++ | 72% ~48s
|+++++++++++++++++++++++++++++++++++++ | 73% ~46s
|+++++++++++++++++++++++++++++++++++++ | 74% ~45s
|++++++++++++++++++++++++++++++++++++++ | 75% ~43s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~41s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~39s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~37s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~35s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~33s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~32s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~30s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~28s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~26s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~24s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~22s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~20s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~19s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~17s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 50s
| | 0 % ~calculating
|+ | 1 % ~08m 04s
|+ | 2 % ~08m 08s
|++ | 3 % ~08m 03s
|++ | 4 % ~07m 59s
|+++ | 5 % ~08m 02s
|+++ | 6 % ~07m 56s
|++++ | 7 % ~07m 51s
|++++ | 8 % ~07m 46s
|+++++ | 9 % ~07m 41s
|+++++ | 10% ~07m 35s
|++++++ | 11% ~07m 31s
|++++++ | 12% ~07m 29s
|+++++++ | 13% ~07m 24s
|+++++++ | 14% ~07m 19s
|++++++++ | 15% ~07m 14s
|++++++++ | 16% ~07m 09s
|+++++++++ | 17% ~07m 09s
|+++++++++ | 18% ~07m 04s
|++++++++++ | 19% ~06m 59s
|++++++++++ | 20% ~06m 53s
|+++++++++++ | 21% ~06m 54s
|+++++++++++ | 22% ~06m 48s
|++++++++++++ | 23% ~06m 42s
|++++++++++++ | 24% ~06m 36s
|+++++++++++++ | 25% ~06m 30s
|+++++++++++++ | 26% ~06m 25s
|++++++++++++++ | 27% ~06m 19s
|++++++++++++++ | 28% ~06m 13s
|+++++++++++++++ | 29% ~06m 08s
|+++++++++++++++ | 30% ~06m 04s
|++++++++++++++++ | 31% ~05m 58s
|++++++++++++++++ | 32% ~05m 53s
|+++++++++++++++++ | 33% ~05m 47s
|+++++++++++++++++ | 34% ~05m 42s
|++++++++++++++++++ | 35% ~05m 36s
|++++++++++++++++++ | 36% ~05m 32s
|+++++++++++++++++++ | 37% ~05m 27s
|+++++++++++++++++++ | 38% ~05m 21s
|++++++++++++++++++++ | 39% ~05m 16s
|++++++++++++++++++++ | 40% ~05m 13s
|+++++++++++++++++++++ | 41% ~05m 08s
|+++++++++++++++++++++ | 42% ~05m 02s
|++++++++++++++++++++++ | 43% ~04m 56s
|++++++++++++++++++++++ | 44% ~04m 51s
|+++++++++++++++++++++++ | 45% ~04m 45s
|+++++++++++++++++++++++ | 46% ~04m 40s
|++++++++++++++++++++++++ | 47% ~04m 35s
|++++++++++++++++++++++++ | 48% ~04m 30s
|+++++++++++++++++++++++++ | 49% ~04m 25s
|+++++++++++++++++++++++++ | 50% ~04m 19s
|++++++++++++++++++++++++++ | 51% ~04m 14s
|++++++++++++++++++++++++++ | 52% ~04m 09s
|+++++++++++++++++++++++++++ | 53% ~04m 04s
|+++++++++++++++++++++++++++ | 54% ~03m 59s
|++++++++++++++++++++++++++++ | 55% ~03m 54s
|++++++++++++++++++++++++++++ | 56% ~03m 48s
|+++++++++++++++++++++++++++++ | 57% ~03m 43s
|+++++++++++++++++++++++++++++ | 58% ~03m 39s
|++++++++++++++++++++++++++++++ | 59% ~03m 33s
|++++++++++++++++++++++++++++++ | 60% ~03m 28s
|+++++++++++++++++++++++++++++++ | 61% ~03m 23s
|+++++++++++++++++++++++++++++++ | 62% ~03m 19s
|++++++++++++++++++++++++++++++++ | 63% ~03m 13s
|++++++++++++++++++++++++++++++++ | 64% ~03m 08s
|+++++++++++++++++++++++++++++++++ | 65% ~03m 03s
|+++++++++++++++++++++++++++++++++ | 66% ~02m 57s
|++++++++++++++++++++++++++++++++++ | 67% ~02m 52s
|++++++++++++++++++++++++++++++++++ | 68% ~02m 47s
|+++++++++++++++++++++++++++++++++++ | 69% ~02m 41s
|+++++++++++++++++++++++++++++++++++ | 70% ~02m 36s
|++++++++++++++++++++++++++++++++++++ | 71% ~02m 31s
|++++++++++++++++++++++++++++++++++++ | 72% ~02m 26s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02m 21s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02m 15s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02m 10s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02m 05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01m 60s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01m 55s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 49s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01m 44s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01m 39s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01m 34s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 29s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01m 23s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01m 18s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 13s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01m 08s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01m 03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~57s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~52s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~47s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~42s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~37s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~31s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~26s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~21s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~16s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 08m 41s
| | 0 % ~calculating
|+ | 1 % ~05m 41s
|++ | 2 % ~05m 40s
|++ | 3 % ~05m 37s
|+++ | 4 % ~05m 33s
|+++ | 5 % ~05m 29s
|++++ | 6 % ~05m 26s
|++++ | 7 % ~05m 23s
|+++++ | 8 % ~05m 20s
|+++++ | 9 % ~05m 21s
|++++++ | 10% ~05m 16s
|++++++ | 11% ~05m 13s
|+++++++ | 12% ~05m 09s
|+++++++ | 13% ~05m 06s
|++++++++ | 14% ~05m 03s
|++++++++ | 15% ~04m 59s
|+++++++++ | 16% ~04m 56s
|+++++++++ | 17% ~04m 53s
|++++++++++ | 18% ~04m 52s
|++++++++++ | 19% ~04m 48s
|+++++++++++ | 20% ~04m 44s
|+++++++++++ | 21% ~04m 40s
|++++++++++++ | 22% ~04m 37s
|++++++++++++ | 23% ~04m 33s
|+++++++++++++ | 24% ~04m 30s
|+++++++++++++ | 26% ~04m 27s
|++++++++++++++ | 27% ~04m 24s
|++++++++++++++ | 28% ~04m 20s
|+++++++++++++++ | 29% ~04m 16s
|+++++++++++++++ | 30% ~04m 13s
|++++++++++++++++ | 31% ~04m 12s
|++++++++++++++++ | 32% ~04m 08s
|+++++++++++++++++ | 33% ~04m 04s
|+++++++++++++++++ | 34% ~04m 01s
|++++++++++++++++++ | 35% ~04m 00s
|++++++++++++++++++ | 36% ~03m 56s
|+++++++++++++++++++ | 37% ~03m 52s
|+++++++++++++++++++ | 38% ~03m 48s
|++++++++++++++++++++ | 39% ~03m 44s
|++++++++++++++++++++ | 40% ~03m 40s
|+++++++++++++++++++++ | 41% ~03m 36s
|+++++++++++++++++++++ | 42% ~03m 32s
|++++++++++++++++++++++ | 43% ~03m 28s
|++++++++++++++++++++++ | 44% ~03m 24s
|+++++++++++++++++++++++ | 45% ~03m 20s
|+++++++++++++++++++++++ | 46% ~03m 16s
|++++++++++++++++++++++++ | 47% ~03m 13s
|++++++++++++++++++++++++ | 48% ~03m 10s
|+++++++++++++++++++++++++ | 49% ~03m 06s
|+++++++++++++++++++++++++ | 50% ~03m 02s
|++++++++++++++++++++++++++ | 51% ~02m 58s
|+++++++++++++++++++++++++++ | 52% ~02m 55s
|+++++++++++++++++++++++++++ | 53% ~02m 51s
|++++++++++++++++++++++++++++ | 54% ~02m 47s
|++++++++++++++++++++++++++++ | 55% ~02m 43s
|+++++++++++++++++++++++++++++ | 56% ~02m 40s
|+++++++++++++++++++++++++++++ | 57% ~02m 36s
|++++++++++++++++++++++++++++++ | 58% ~02m 32s
|++++++++++++++++++++++++++++++ | 59% ~02m 29s
|+++++++++++++++++++++++++++++++ | 60% ~02m 25s
|+++++++++++++++++++++++++++++++ | 61% ~02m 21s
|++++++++++++++++++++++++++++++++ | 62% ~02m 18s
|++++++++++++++++++++++++++++++++ | 63% ~02m 14s
|+++++++++++++++++++++++++++++++++ | 64% ~02m 10s
|+++++++++++++++++++++++++++++++++ | 65% ~02m 06s
|++++++++++++++++++++++++++++++++++ | 66% ~02m 04s
|++++++++++++++++++++++++++++++++++ | 67% ~01m 60s
|+++++++++++++++++++++++++++++++++++ | 68% ~01m 56s
|+++++++++++++++++++++++++++++++++++ | 69% ~01m 52s
|++++++++++++++++++++++++++++++++++++ | 70% ~01m 48s
|++++++++++++++++++++++++++++++++++++ | 71% ~01m 44s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01m 41s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01m 37s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01m 33s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01m 29s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01m 26s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01m 22s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 18s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01m 15s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01m 11s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01m 07s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~60s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~56s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~52s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~48s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~45s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~41s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~37s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~34s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~30s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~26s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~19s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 06m 04s
| | 0 % ~calculating
|+ | 1 % ~01m 33s
|++ | 2 % ~01m 31s
|++ | 3 % ~01m 28s
|+++ | 4 % ~01m 27s
|+++ | 6 % ~01m 25s
|++++ | 7 % ~01m 25s
|++++ | 8 % ~01m 24s
|+++++ | 9 % ~01m 22s
|++++++ | 10% ~01m 21s
|++++++ | 11% ~01m 20s
|+++++++ | 12% ~01m 19s
|+++++++ | 13% ~01m 18s
|++++++++ | 15% ~01m 17s
|++++++++ | 16% ~01m 16s
|+++++++++ | 17% ~01m 15s
|+++++++++ | 18% ~01m 14s
|++++++++++ | 19% ~01m 13s
|+++++++++++ | 20% ~01m 12s
|+++++++++++ | 21% ~01m 11s
|++++++++++++ | 22% ~01m 10s
|++++++++++++ | 24% ~01m 09s
|+++++++++++++ | 25% ~01m 08s
|+++++++++++++ | 26% ~01m 07s
|++++++++++++++ | 27% ~01m 06s
|+++++++++++++++ | 28% ~01m 06s
|+++++++++++++++ | 29% ~01m 05s
|++++++++++++++++ | 30% ~01m 04s
|++++++++++++++++ | 31% ~01m 03s
|+++++++++++++++++ | 33% ~01m 02s
|+++++++++++++++++ | 34% ~01m 01s
|++++++++++++++++++ | 35% ~60s
|++++++++++++++++++ | 36% ~59s
|+++++++++++++++++++ | 37% ~58s
|++++++++++++++++++++ | 38% ~57s
|++++++++++++++++++++ | 39% ~56s
|+++++++++++++++++++++ | 40% ~55s
|+++++++++++++++++++++ | 42% ~54s
|++++++++++++++++++++++ | 43% ~53s
|++++++++++++++++++++++ | 44% ~52s
|+++++++++++++++++++++++ | 45% ~51s
|++++++++++++++++++++++++ | 46% ~50s
|++++++++++++++++++++++++ | 47% ~49s
|+++++++++++++++++++++++++ | 48% ~48s
|+++++++++++++++++++++++++ | 49% ~47s
|++++++++++++++++++++++++++ | 51% ~46s
|++++++++++++++++++++++++++ | 52% ~45s
|+++++++++++++++++++++++++++ | 53% ~44s
|+++++++++++++++++++++++++++ | 54% ~43s
|++++++++++++++++++++++++++++ | 55% ~42s
|+++++++++++++++++++++++++++++ | 56% ~41s
|+++++++++++++++++++++++++++++ | 57% ~40s
|++++++++++++++++++++++++++++++ | 58% ~39s
|++++++++++++++++++++++++++++++ | 60% ~38s
|+++++++++++++++++++++++++++++++ | 61% ~37s
|+++++++++++++++++++++++++++++++ | 62% ~36s
|++++++++++++++++++++++++++++++++ | 63% ~35s
|+++++++++++++++++++++++++++++++++ | 64% ~34s
|+++++++++++++++++++++++++++++++++ | 65% ~33s
|++++++++++++++++++++++++++++++++++ | 66% ~32s
|++++++++++++++++++++++++++++++++++ | 67% ~31s
|+++++++++++++++++++++++++++++++++++ | 69% ~30s
|+++++++++++++++++++++++++++++++++++ | 70% ~29s
|++++++++++++++++++++++++++++++++++++ | 71% ~27s
|++++++++++++++++++++++++++++++++++++ | 72% ~26s
|+++++++++++++++++++++++++++++++++++++ | 73% ~25s
|++++++++++++++++++++++++++++++++++++++ | 74% ~24s
|++++++++++++++++++++++++++++++++++++++ | 75% ~23s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~22s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~21s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~20s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~19s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~18s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~17s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~16s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~15s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~13s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 35s
| | 0 % ~calculating
|+ | 1 % ~01m 02s
|++ | 2 % ~01m 01s
|++ | 3 % ~60s
|+++ | 5 % ~59s
|+++ | 6 % ~58s
|++++ | 7 % ~58s
|+++++ | 8 % ~57s
|+++++ | 9 % ~56s
|++++++ | 10% ~56s
|++++++ | 11% ~55s
|+++++++ | 13% ~54s
|+++++++ | 14% ~53s
|++++++++ | 15% ~53s
|+++++++++ | 16% ~52s
|+++++++++ | 17% ~51s
|++++++++++ | 18% ~50s
|++++++++++ | 20% ~50s
|+++++++++++ | 21% ~49s
|+++++++++++ | 22% ~48s
|++++++++++++ | 23% ~48s
|+++++++++++++ | 24% ~47s
|+++++++++++++ | 25% ~46s
|++++++++++++++ | 26% ~45s
|++++++++++++++ | 28% ~46s
|+++++++++++++++ | 29% ~45s
|+++++++++++++++ | 30% ~44s
|++++++++++++++++ | 31% ~43s
|+++++++++++++++++ | 32% ~43s
|+++++++++++++++++ | 33% ~42s
|++++++++++++++++++ | 34% ~41s
|++++++++++++++++++ | 36% ~41s
|+++++++++++++++++++ | 37% ~40s
|+++++++++++++++++++ | 38% ~39s
|++++++++++++++++++++ | 39% ~38s
|+++++++++++++++++++++ | 40% ~38s
|+++++++++++++++++++++ | 41% ~37s
|++++++++++++++++++++++ | 43% ~36s
|++++++++++++++++++++++ | 44% ~36s
|+++++++++++++++++++++++ | 45% ~35s
|+++++++++++++++++++++++ | 46% ~34s
|++++++++++++++++++++++++ | 47% ~33s
|+++++++++++++++++++++++++ | 48% ~33s
|+++++++++++++++++++++++++ | 49% ~32s
|++++++++++++++++++++++++++ | 51% ~31s
|++++++++++++++++++++++++++ | 52% ~31s
|+++++++++++++++++++++++++++ | 53% ~30s
|++++++++++++++++++++++++++++ | 54% ~29s
|++++++++++++++++++++++++++++ | 55% ~28s
|+++++++++++++++++++++++++++++ | 56% ~28s
|+++++++++++++++++++++++++++++ | 57% ~27s
|++++++++++++++++++++++++++++++ | 59% ~26s
|++++++++++++++++++++++++++++++ | 60% ~26s
|+++++++++++++++++++++++++++++++ | 61% ~25s
|++++++++++++++++++++++++++++++++ | 62% ~24s
|++++++++++++++++++++++++++++++++ | 63% ~23s
|+++++++++++++++++++++++++++++++++ | 64% ~23s
|+++++++++++++++++++++++++++++++++ | 66% ~22s
|++++++++++++++++++++++++++++++++++ | 67% ~21s
|++++++++++++++++++++++++++++++++++ | 68% ~20s
|+++++++++++++++++++++++++++++++++++ | 69% ~20s
|++++++++++++++++++++++++++++++++++++ | 70% ~19s
|++++++++++++++++++++++++++++++++++++ | 71% ~18s
|+++++++++++++++++++++++++++++++++++++ | 72% ~18s
|+++++++++++++++++++++++++++++++++++++ | 74% ~17s
|++++++++++++++++++++++++++++++++++++++ | 75% ~16s
|++++++++++++++++++++++++++++++++++++++ | 76% ~16s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~15s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~14s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~13s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~13s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~12s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~11s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~10s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 04s
| | 0 % ~calculating
|+ | 1 % ~01m 32s
|++ | 2 % ~01m 32s
|++ | 3 % ~01m 31s
|+++ | 4 % ~01m 29s
|+++ | 6 % ~01m 28s
|++++ | 7 % ~01m 28s
|++++ | 8 % ~01m 26s
|+++++ | 9 % ~01m 25s
|+++++ | 10% ~01m 24s
|++++++ | 11% ~01m 23s
|+++++++ | 12% ~01m 22s
|+++++++ | 13% ~01m 21s
|++++++++ | 14% ~01m 20s
|++++++++ | 16% ~01m 19s
|+++++++++ | 17% ~01m 18s
|+++++++++ | 18% ~01m 17s
|++++++++++ | 19% ~01m 16s
|++++++++++ | 20% ~01m 16s
|+++++++++++ | 21% ~01m 15s
|++++++++++++ | 22% ~01m 14s
|++++++++++++ | 23% ~01m 13s
|+++++++++++++ | 24% ~01m 12s
|+++++++++++++ | 26% ~01m 11s
|++++++++++++++ | 27% ~01m 10s
|++++++++++++++ | 28% ~01m 08s
|+++++++++++++++ | 29% ~01m 07s
|+++++++++++++++ | 30% ~01m 06s
|++++++++++++++++ | 31% ~01m 05s
|+++++++++++++++++ | 32% ~01m 04s
|+++++++++++++++++ | 33% ~01m 03s
|++++++++++++++++++ | 34% ~01m 02s
|++++++++++++++++++ | 36% ~01m 01s
|+++++++++++++++++++ | 37% ~01m 00s
|+++++++++++++++++++ | 38% ~59s
|++++++++++++++++++++ | 39% ~58s
|++++++++++++++++++++ | 40% ~57s
|+++++++++++++++++++++ | 41% ~56s
|++++++++++++++++++++++ | 42% ~55s
|++++++++++++++++++++++ | 43% ~54s
|+++++++++++++++++++++++ | 44% ~53s
|+++++++++++++++++++++++ | 46% ~52s
|++++++++++++++++++++++++ | 47% ~51s
|++++++++++++++++++++++++ | 48% ~50s
|+++++++++++++++++++++++++ | 49% ~49s
|+++++++++++++++++++++++++ | 50% ~48s
|++++++++++++++++++++++++++ | 51% ~47s
|+++++++++++++++++++++++++++ | 52% ~45s
|+++++++++++++++++++++++++++ | 53% ~44s
|++++++++++++++++++++++++++++ | 54% ~44s
|++++++++++++++++++++++++++++ | 56% ~43s
|+++++++++++++++++++++++++++++ | 57% ~42s
|+++++++++++++++++++++++++++++ | 58% ~41s
|++++++++++++++++++++++++++++++ | 59% ~40s
|++++++++++++++++++++++++++++++ | 60% ~39s
|+++++++++++++++++++++++++++++++ | 61% ~37s
|++++++++++++++++++++++++++++++++ | 62% ~36s
|++++++++++++++++++++++++++++++++ | 63% ~35s
|+++++++++++++++++++++++++++++++++ | 64% ~34s
|+++++++++++++++++++++++++++++++++ | 66% ~33s
|++++++++++++++++++++++++++++++++++ | 67% ~32s
|++++++++++++++++++++++++++++++++++ | 68% ~31s
|+++++++++++++++++++++++++++++++++++ | 69% ~30s
|+++++++++++++++++++++++++++++++++++ | 70% ~29s
|++++++++++++++++++++++++++++++++++++ | 71% ~28s
|+++++++++++++++++++++++++++++++++++++ | 72% ~27s
|+++++++++++++++++++++++++++++++++++++ | 73% ~26s
|++++++++++++++++++++++++++++++++++++++ | 74% ~25s
|++++++++++++++++++++++++++++++++++++++ | 76% ~24s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~23s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~21s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~20s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~19s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~18s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~17s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~16s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~15s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~13s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 38s
| | 0 % ~calculating
|+ | 1 % ~04m 29s
|++ | 2 % ~04m 24s
|++ | 3 % ~04m 21s
|+++ | 4 % ~04m 20s
|+++ | 5 % ~04m 17s
|++++ | 6 % ~04m 16s
|++++ | 7 % ~04m 12s
|+++++ | 8 % ~04m 10s
|+++++ | 9 % ~04m 07s
|++++++ | 10% ~04m 05s
|++++++ | 11% ~04m 02s
|+++++++ | 12% ~03m 59s
|+++++++ | 13% ~03m 57s
|++++++++ | 14% ~03m 58s
|++++++++ | 15% ~03m 55s
|+++++++++ | 16% ~03m 53s
|+++++++++ | 17% ~03m 50s
|++++++++++ | 18% ~03m 47s
|++++++++++ | 19% ~03m 45s
|+++++++++++ | 20% ~03m 42s
|+++++++++++ | 21% ~03m 39s
|++++++++++++ | 22% ~03m 36s
|++++++++++++ | 23% ~03m 33s
|+++++++++++++ | 24% ~03m 32s
|+++++++++++++ | 25% ~03m 29s
|++++++++++++++ | 26% ~03m 27s
|++++++++++++++ | 27% ~03m 24s
|+++++++++++++++ | 28% ~03m 21s
|+++++++++++++++ | 29% ~03m 18s
|++++++++++++++++ | 30% ~03m 15s
|++++++++++++++++ | 31% ~03m 17s
|+++++++++++++++++ | 32% ~03m 13s
|+++++++++++++++++ | 33% ~03m 10s
|++++++++++++++++++ | 34% ~03m 07s
|++++++++++++++++++ | 35% ~03m 04s
|+++++++++++++++++++ | 36% ~03m 01s
|+++++++++++++++++++ | 37% ~02m 58s
|++++++++++++++++++++ | 38% ~02m 55s
|++++++++++++++++++++ | 39% ~02m 52s
|+++++++++++++++++++++ | 40% ~02m 49s
|+++++++++++++++++++++ | 41% ~02m 46s
|++++++++++++++++++++++ | 42% ~02m 43s
|++++++++++++++++++++++ | 43% ~02m 40s
|+++++++++++++++++++++++ | 44% ~02m 37s
|+++++++++++++++++++++++ | 45% ~02m 35s
|++++++++++++++++++++++++ | 46% ~02m 32s
|++++++++++++++++++++++++ | 47% ~02m 29s
|+++++++++++++++++++++++++ | 48% ~02m 26s
|+++++++++++++++++++++++++ | 49% ~02m 23s
|++++++++++++++++++++++++++ | 51% ~02m 20s
|++++++++++++++++++++++++++ | 52% ~02m 17s
|+++++++++++++++++++++++++++ | 53% ~02m 14s
|+++++++++++++++++++++++++++ | 54% ~02m 12s
|++++++++++++++++++++++++++++ | 55% ~02m 09s
|++++++++++++++++++++++++++++ | 56% ~02m 06s
|+++++++++++++++++++++++++++++ | 57% ~02m 03s
|+++++++++++++++++++++++++++++ | 58% ~02m 00s
|++++++++++++++++++++++++++++++ | 59% ~01m 58s
|++++++++++++++++++++++++++++++ | 60% ~01m 55s
|+++++++++++++++++++++++++++++++ | 61% ~01m 52s
|+++++++++++++++++++++++++++++++ | 62% ~01m 49s
|++++++++++++++++++++++++++++++++ | 63% ~01m 47s
|++++++++++++++++++++++++++++++++ | 64% ~01m 44s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 41s
|+++++++++++++++++++++++++++++++++ | 66% ~01m 38s
|++++++++++++++++++++++++++++++++++ | 67% ~01m 35s
|++++++++++++++++++++++++++++++++++ | 68% ~01m 32s
|+++++++++++++++++++++++++++++++++++ | 69% ~01m 29s
|+++++++++++++++++++++++++++++++++++ | 70% ~01m 27s
|++++++++++++++++++++++++++++++++++++ | 71% ~01m 24s
|++++++++++++++++++++++++++++++++++++ | 72% ~01m 21s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01m 18s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01m 15s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01m 12s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01m 09s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01m 06s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01m 03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~58s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~55s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~52s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~49s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~46s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~43s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~40s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~37s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~35s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~32s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~29s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~26s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~23s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~20s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~17s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 04m 43s
| | 0 % ~calculating
|+ | 1 % ~04m 26s
|++ | 2 % ~04m 26s
|++ | 3 % ~04m 23s
|+++ | 4 % ~04m 20s
|+++ | 5 % ~04m 17s
|++++ | 6 % ~04m 15s
|++++ | 7 % ~04m 13s
|+++++ | 8 % ~04m 10s
|+++++ | 9 % ~04m 07s
|++++++ | 10% ~04m 05s
|++++++ | 11% ~04m 02s
|+++++++ | 12% ~03m 59s
|+++++++ | 13% ~03m 57s
|++++++++ | 14% ~03m 57s
|++++++++ | 15% ~03m 55s
|+++++++++ | 16% ~03m 52s
|+++++++++ | 17% ~03m 49s
|++++++++++ | 18% ~03m 46s
|++++++++++ | 19% ~03m 44s
|+++++++++++ | 20% ~03m 41s
|+++++++++++ | 21% ~03m 38s
|++++++++++++ | 22% ~03m 35s
|++++++++++++ | 23% ~03m 33s
|+++++++++++++ | 24% ~03m 30s
|+++++++++++++ | 25% ~03m 29s
|++++++++++++++ | 26% ~03m 26s
|++++++++++++++ | 27% ~03m 23s
|+++++++++++++++ | 28% ~03m 20s
|+++++++++++++++ | 29% ~03m 17s
|++++++++++++++++ | 30% ~03m 14s
|++++++++++++++++ | 31% ~03m 12s
|+++++++++++++++++ | 32% ~03m 11s
|+++++++++++++++++ | 33% ~03m 08s
|++++++++++++++++++ | 34% ~03m 05s
|++++++++++++++++++ | 35% ~03m 02s
|+++++++++++++++++++ | 36% ~02m 59s
|+++++++++++++++++++ | 37% ~02m 57s
|++++++++++++++++++++ | 38% ~02m 54s
|++++++++++++++++++++ | 39% ~02m 53s
|+++++++++++++++++++++ | 40% ~02m 50s
|+++++++++++++++++++++ | 41% ~02m 47s
|++++++++++++++++++++++ | 42% ~02m 44s
|++++++++++++++++++++++ | 43% ~02m 41s
|+++++++++++++++++++++++ | 44% ~02m 38s
|+++++++++++++++++++++++ | 45% ~02m 35s
|++++++++++++++++++++++++ | 46% ~02m 32s
|++++++++++++++++++++++++ | 47% ~02m 29s
|+++++++++++++++++++++++++ | 48% ~02m 26s
|+++++++++++++++++++++++++ | 49% ~02m 23s
|++++++++++++++++++++++++++ | 51% ~02m 21s
|++++++++++++++++++++++++++ | 52% ~02m 18s
|+++++++++++++++++++++++++++ | 53% ~02m 15s
|+++++++++++++++++++++++++++ | 54% ~02m 12s
|++++++++++++++++++++++++++++ | 55% ~02m 09s
|++++++++++++++++++++++++++++ | 56% ~02m 06s
|+++++++++++++++++++++++++++++ | 57% ~02m 03s
|+++++++++++++++++++++++++++++ | 58% ~02m 01s
|++++++++++++++++++++++++++++++ | 59% ~01m 58s
|++++++++++++++++++++++++++++++ | 60% ~01m 55s
|+++++++++++++++++++++++++++++++ | 61% ~01m 52s
|+++++++++++++++++++++++++++++++ | 62% ~01m 49s
|++++++++++++++++++++++++++++++++ | 63% ~01m 46s
|++++++++++++++++++++++++++++++++ | 64% ~01m 43s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 41s
|+++++++++++++++++++++++++++++++++ | 66% ~01m 38s
|++++++++++++++++++++++++++++++++++ | 67% ~01m 35s
|++++++++++++++++++++++++++++++++++ | 68% ~01m 32s
|+++++++++++++++++++++++++++++++++++ | 69% ~01m 29s
|+++++++++++++++++++++++++++++++++++ | 70% ~01m 26s
|++++++++++++++++++++++++++++++++++++ | 71% ~01m 24s
|++++++++++++++++++++++++++++++++++++ | 72% ~01m 21s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01m 18s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01m 15s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01m 12s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01m 09s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01m 07s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01m 04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~58s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~55s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~52s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~49s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~46s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~43s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~40s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~37s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~35s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~32s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~29s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~26s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~23s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~20s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~17s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 04m 43s
| | 0 % ~calculating
|+ | 1 % ~01m 41s
|++ | 2 % ~01m 40s
|++ | 3 % ~01m 39s
|+++ | 4 % ~01m 38s
|+++ | 5 % ~01m 36s
|++++ | 7 % ~01m 36s
|++++ | 8 % ~01m 34s
|+++++ | 9 % ~01m 33s
|+++++ | 10% ~01m 32s
|++++++ | 11% ~01m 30s
|++++++ | 12% ~01m 32s
|+++++++ | 13% ~01m 31s
|++++++++ | 14% ~01m 30s
|++++++++ | 15% ~01m 28s
|+++++++++ | 16% ~01m 27s
|+++++++++ | 17% ~01m 26s
|++++++++++ | 18% ~01m 25s
|++++++++++ | 20% ~01m 24s
|+++++++++++ | 21% ~01m 22s
|+++++++++++ | 22% ~01m 21s
|++++++++++++ | 23% ~01m 20s
|++++++++++++ | 24% ~01m 25s
|+++++++++++++ | 25% ~01m 24s
|++++++++++++++ | 26% ~01m 22s
|++++++++++++++ | 27% ~01m 20s
|+++++++++++++++ | 28% ~01m 19s
|+++++++++++++++ | 29% ~01m 17s
|++++++++++++++++ | 30% ~01m 16s
|++++++++++++++++ | 32% ~01m 15s
|+++++++++++++++++ | 33% ~01m 13s
|+++++++++++++++++ | 34% ~01m 12s
|++++++++++++++++++ | 35% ~01m 11s
|++++++++++++++++++ | 36% ~01m 09s
|+++++++++++++++++++ | 37% ~01m 08s
|++++++++++++++++++++ | 38% ~01m 07s
|++++++++++++++++++++ | 39% ~01m 05s
|+++++++++++++++++++++ | 40% ~01m 04s
|+++++++++++++++++++++ | 41% ~01m 03s
|++++++++++++++++++++++ | 42% ~01m 02s
|++++++++++++++++++++++ | 43% ~01m 00s
|+++++++++++++++++++++++ | 45% ~59s
|+++++++++++++++++++++++ | 46% ~58s
|++++++++++++++++++++++++ | 47% ~57s
|++++++++++++++++++++++++ | 48% ~56s
|+++++++++++++++++++++++++ | 49% ~54s
|+++++++++++++++++++++++++ | 50% ~53s
|++++++++++++++++++++++++++ | 51% ~52s
|+++++++++++++++++++++++++++ | 52% ~51s
|+++++++++++++++++++++++++++ | 53% ~49s
|++++++++++++++++++++++++++++ | 54% ~48s
|++++++++++++++++++++++++++++ | 55% ~47s
|+++++++++++++++++++++++++++++ | 57% ~46s
|+++++++++++++++++++++++++++++ | 58% ~45s
|++++++++++++++++++++++++++++++ | 59% ~44s
|++++++++++++++++++++++++++++++ | 60% ~43s
|+++++++++++++++++++++++++++++++ | 61% ~42s
|+++++++++++++++++++++++++++++++ | 62% ~40s
|++++++++++++++++++++++++++++++++ | 63% ~39s
|+++++++++++++++++++++++++++++++++ | 64% ~38s
|+++++++++++++++++++++++++++++++++ | 65% ~37s
|++++++++++++++++++++++++++++++++++ | 66% ~36s
|++++++++++++++++++++++++++++++++++ | 67% ~35s
|+++++++++++++++++++++++++++++++++++ | 68% ~33s
|+++++++++++++++++++++++++++++++++++ | 70% ~32s
|++++++++++++++++++++++++++++++++++++ | 71% ~31s
|++++++++++++++++++++++++++++++++++++ | 72% ~30s
|+++++++++++++++++++++++++++++++++++++ | 73% ~29s
|+++++++++++++++++++++++++++++++++++++ | 74% ~28s
|++++++++++++++++++++++++++++++++++++++ | 75% ~26s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~25s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~24s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~23s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~22s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~21s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~20s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~18s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~17s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~16s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~15s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~14s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 46s
| | 0 % ~calculating
|+ | 1 % ~01m 50s
|++ | 2 % ~01m 51s
|++ | 3 % ~01m 50s
|+++ | 4 % ~01m 49s
|+++ | 5 % ~01m 48s
|++++ | 6 % ~01m 48s
|++++ | 7 % ~01m 46s
|+++++ | 8 % ~01m 45s
|+++++ | 9 % ~01m 43s
|++++++ | 11% ~01m 42s
|++++++ | 12% ~01m 40s
|+++++++ | 13% ~01m 39s
|+++++++ | 14% ~01m 38s
|++++++++ | 15% ~01m 37s
|++++++++ | 16% ~01m 36s
|+++++++++ | 17% ~01m 35s
|+++++++++ | 18% ~01m 34s
|++++++++++ | 19% ~01m 33s
|++++++++++ | 20% ~01m 32s
|+++++++++++ | 21% ~01m 30s
|++++++++++++ | 22% ~01m 29s
|++++++++++++ | 23% ~01m 30s
|+++++++++++++ | 24% ~01m 29s
|+++++++++++++ | 25% ~01m 27s
|++++++++++++++ | 26% ~01m 26s
|++++++++++++++ | 27% ~01m 25s
|+++++++++++++++ | 28% ~01m 24s
|+++++++++++++++ | 29% ~01m 22s
|++++++++++++++++ | 31% ~01m 21s
|++++++++++++++++ | 32% ~01m 20s
|+++++++++++++++++ | 33% ~01m 19s
|+++++++++++++++++ | 34% ~01m 18s
|++++++++++++++++++ | 35% ~01m 16s
|++++++++++++++++++ | 36% ~01m 15s
|+++++++++++++++++++ | 37% ~01m 14s
|+++++++++++++++++++ | 38% ~01m 13s
|++++++++++++++++++++ | 39% ~01m 12s
|++++++++++++++++++++ | 40% ~01m 10s
|+++++++++++++++++++++ | 41% ~01m 09s
|++++++++++++++++++++++ | 42% ~01m 08s
|++++++++++++++++++++++ | 43% ~01m 06s
|+++++++++++++++++++++++ | 44% ~01m 05s
|+++++++++++++++++++++++ | 45% ~01m 05s
|++++++++++++++++++++++++ | 46% ~01m 03s
|++++++++++++++++++++++++ | 47% ~01m 02s
|+++++++++++++++++++++++++ | 48% ~01m 01s
|+++++++++++++++++++++++++ | 49% ~60s
|++++++++++++++++++++++++++ | 51% ~58s
|++++++++++++++++++++++++++ | 52% ~57s
|+++++++++++++++++++++++++++ | 53% ~56s
|+++++++++++++++++++++++++++ | 54% ~55s
|++++++++++++++++++++++++++++ | 55% ~53s
|++++++++++++++++++++++++++++ | 56% ~52s
|+++++++++++++++++++++++++++++ | 57% ~51s
|+++++++++++++++++++++++++++++ | 58% ~50s
|++++++++++++++++++++++++++++++ | 59% ~48s
|++++++++++++++++++++++++++++++ | 60% ~47s
|+++++++++++++++++++++++++++++++ | 61% ~47s
|++++++++++++++++++++++++++++++++ | 62% ~45s
|++++++++++++++++++++++++++++++++ | 63% ~44s
|+++++++++++++++++++++++++++++++++ | 64% ~43s
|+++++++++++++++++++++++++++++++++ | 65% ~42s
|++++++++++++++++++++++++++++++++++ | 66% ~40s
|++++++++++++++++++++++++++++++++++ | 67% ~39s
|+++++++++++++++++++++++++++++++++++ | 68% ~38s
|+++++++++++++++++++++++++++++++++++ | 69% ~36s
|++++++++++++++++++++++++++++++++++++ | 71% ~35s
|++++++++++++++++++++++++++++++++++++ | 72% ~34s
|+++++++++++++++++++++++++++++++++++++ | 73% ~33s
|+++++++++++++++++++++++++++++++++++++ | 74% ~31s
|++++++++++++++++++++++++++++++++++++++ | 75% ~31s
|++++++++++++++++++++++++++++++++++++++ | 76% ~29s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~28s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~27s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~26s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~24s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~23s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~22s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~20s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~19s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~18s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~16s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~14s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 59s
| | 0 % ~calculating
|+ | 1 % ~02m 44s
|++ | 2 % ~02m 46s
|++ | 3 % ~02m 45s
|+++ | 4 % ~02m 43s
|+++ | 5 % ~02m 42s
|++++ | 6 % ~02m 40s
|++++ | 7 % ~02m 38s
|+++++ | 8 % ~02m 36s
|+++++ | 9 % ~02m 39s
|++++++ | 10% ~02m 36s
|++++++ | 11% ~02m 34s
|+++++++ | 12% ~02m 31s
|+++++++ | 13% ~02m 30s
|++++++++ | 14% ~02m 28s
|++++++++ | 15% ~02m 26s
|+++++++++ | 16% ~02m 24s
|+++++++++ | 18% ~02m 22s
|++++++++++ | 19% ~02m 21s
|++++++++++ | 20% ~02m 19s
|+++++++++++ | 21% ~02m 17s
|+++++++++++ | 22% ~02m 17s
|++++++++++++ | 23% ~02m 15s
|++++++++++++ | 24% ~02m 13s
|+++++++++++++ | 25% ~02m 11s
|+++++++++++++ | 26% ~02m 09s
|++++++++++++++ | 27% ~02m 07s
|++++++++++++++ | 28% ~02m 06s
|+++++++++++++++ | 29% ~02m 04s
|+++++++++++++++ | 30% ~02m 05s
|++++++++++++++++ | 31% ~02m 03s
|++++++++++++++++ | 32% ~02m 01s
|+++++++++++++++++ | 33% ~01m 59s
|++++++++++++++++++ | 34% ~01m 57s
|++++++++++++++++++ | 35% ~01m 55s
|+++++++++++++++++++ | 36% ~01m 53s
|+++++++++++++++++++ | 37% ~01m 51s
|++++++++++++++++++++ | 38% ~01m 52s
|++++++++++++++++++++ | 39% ~01m 50s
|+++++++++++++++++++++ | 40% ~01m 48s
|+++++++++++++++++++++ | 41% ~01m 46s
|++++++++++++++++++++++ | 42% ~01m 44s
|++++++++++++++++++++++ | 43% ~01m 42s
|+++++++++++++++++++++++ | 44% ~01m 40s
|+++++++++++++++++++++++ | 45% ~01m 38s
|++++++++++++++++++++++++ | 46% ~01m 36s
|++++++++++++++++++++++++ | 47% ~01m 34s
|+++++++++++++++++++++++++ | 48% ~01m 32s
|+++++++++++++++++++++++++ | 49% ~01m 30s
|++++++++++++++++++++++++++ | 51% ~01m 28s
|++++++++++++++++++++++++++ | 52% ~01m 26s
|+++++++++++++++++++++++++++ | 53% ~01m 24s
|+++++++++++++++++++++++++++ | 54% ~01m 22s
|++++++++++++++++++++++++++++ | 55% ~01m 20s
|++++++++++++++++++++++++++++ | 56% ~01m 18s
|+++++++++++++++++++++++++++++ | 57% ~01m 17s
|+++++++++++++++++++++++++++++ | 58% ~01m 15s
|++++++++++++++++++++++++++++++ | 59% ~01m 13s
|++++++++++++++++++++++++++++++ | 60% ~01m 11s
|+++++++++++++++++++++++++++++++ | 61% ~01m 09s
|+++++++++++++++++++++++++++++++ | 62% ~01m 08s
|++++++++++++++++++++++++++++++++ | 63% ~01m 06s
|++++++++++++++++++++++++++++++++ | 64% ~01m 04s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 02s
|+++++++++++++++++++++++++++++++++ | 66% ~01m 00s
|++++++++++++++++++++++++++++++++++ | 67% ~58s
|+++++++++++++++++++++++++++++++++++ | 68% ~56s
|+++++++++++++++++++++++++++++++++++ | 69% ~55s
|++++++++++++++++++++++++++++++++++++ | 70% ~53s
|++++++++++++++++++++++++++++++++++++ | 71% ~51s
|+++++++++++++++++++++++++++++++++++++ | 72% ~49s
|+++++++++++++++++++++++++++++++++++++ | 73% ~47s
|++++++++++++++++++++++++++++++++++++++ | 74% ~45s
|++++++++++++++++++++++++++++++++++++++ | 75% ~43s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~42s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~40s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~38s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~36s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~34s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~33s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~31s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~29s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~27s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~25s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~24s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~22s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~20s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~18s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~16s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 58s
| | 0 % ~calculating
|+ | 1 % ~02m 13s
|++ | 2 % ~02m 12s
|++ | 3 % ~02m 10s
|+++ | 4 % ~02m 09s
|+++ | 5 % ~02m 08s
|++++ | 7 % ~02m 07s
|++++ | 8 % ~02m 05s
|+++++ | 9 % ~02m 07s
|+++++ | 10% ~02m 05s
|++++++ | 11% ~02m 04s
|+++++++ | 12% ~02m 02s
|+++++++ | 13% ~02m 00s
|++++++++ | 14% ~01m 59s
|++++++++ | 15% ~01m 57s
|+++++++++ | 16% ~01m 55s
|+++++++++ | 18% ~01m 54s
|++++++++++ | 19% ~01m 52s
|++++++++++ | 20% ~01m 51s
|+++++++++++ | 21% ~01m 49s
|+++++++++++ | 22% ~01m 48s
|++++++++++++ | 23% ~01m 46s
|+++++++++++++ | 24% ~01m 45s
|+++++++++++++ | 25% ~01m 43s
|++++++++++++++ | 26% ~01m 42s
|++++++++++++++ | 27% ~01m 41s
|+++++++++++++++ | 29% ~01m 39s
|+++++++++++++++ | 30% ~01m 39s
|++++++++++++++++ | 31% ~01m 37s
|++++++++++++++++ | 32% ~01m 36s
|+++++++++++++++++ | 33% ~01m 34s
|++++++++++++++++++ | 34% ~01m 32s
|++++++++++++++++++ | 35% ~01m 31s
|+++++++++++++++++++ | 36% ~01m 30s
|+++++++++++++++++++ | 37% ~01m 28s
|++++++++++++++++++++ | 38% ~01m 27s
|++++++++++++++++++++ | 40% ~01m 25s
|+++++++++++++++++++++ | 41% ~01m 23s
|+++++++++++++++++++++ | 42% ~01m 22s
|++++++++++++++++++++++ | 43% ~01m 21s
|++++++++++++++++++++++ | 44% ~01m 19s
|+++++++++++++++++++++++ | 45% ~01m 18s
|++++++++++++++++++++++++ | 46% ~01m 16s
|++++++++++++++++++++++++ | 47% ~01m 15s
|+++++++++++++++++++++++++ | 48% ~01m 13s
|+++++++++++++++++++++++++ | 49% ~01m 12s
|++++++++++++++++++++++++++ | 51% ~01m 11s
|++++++++++++++++++++++++++ | 52% ~01m 10s
|+++++++++++++++++++++++++++ | 53% ~01m 08s
|+++++++++++++++++++++++++++ | 54% ~01m 06s
|++++++++++++++++++++++++++++ | 55% ~01m 05s
|+++++++++++++++++++++++++++++ | 56% ~01m 03s
|+++++++++++++++++++++++++++++ | 57% ~01m 02s
|++++++++++++++++++++++++++++++ | 58% ~01m 00s
|++++++++++++++++++++++++++++++ | 59% ~59s
|+++++++++++++++++++++++++++++++ | 60% ~58s
|+++++++++++++++++++++++++++++++ | 62% ~56s
|++++++++++++++++++++++++++++++++ | 63% ~55s
|++++++++++++++++++++++++++++++++ | 64% ~53s
|+++++++++++++++++++++++++++++++++ | 65% ~51s
|+++++++++++++++++++++++++++++++++ | 66% ~50s
|++++++++++++++++++++++++++++++++++ | 67% ~48s
|+++++++++++++++++++++++++++++++++++ | 68% ~46s
|+++++++++++++++++++++++++++++++++++ | 69% ~45s
|++++++++++++++++++++++++++++++++++++ | 70% ~43s
|++++++++++++++++++++++++++++++++++++ | 71% ~42s
|+++++++++++++++++++++++++++++++++++++ | 73% ~40s
|+++++++++++++++++++++++++++++++++++++ | 74% ~38s
|++++++++++++++++++++++++++++++++++++++ | 75% ~37s
|++++++++++++++++++++++++++++++++++++++ | 76% ~35s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~33s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~32s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~30s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~29s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~27s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~25s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~24s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~22s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~21s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~19s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~18s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~16s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 24s
| | 0 % ~calculating
|+ | 1 % ~04m 06s
|++ | 2 % ~04m 01s
|++ | 3 % ~03m 56s
|+++ | 4 % ~03m 53s
|+++ | 5 % ~03m 51s
|++++ | 6 % ~03m 48s
|++++ | 7 % ~03m 46s
|+++++ | 9 % ~03m 48s
|+++++ | 10% ~03m 45s
|++++++ | 11% ~03m 42s
|++++++ | 12% ~03m 39s
|+++++++ | 13% ~03m 36s
|+++++++ | 14% ~03m 34s
|++++++++ | 15% ~03m 31s
|++++++++ | 16% ~03m 28s
|+++++++++ | 17% ~03m 25s
|++++++++++ | 18% ~03m 23s
|++++++++++ | 19% ~03m 22s
|+++++++++++ | 20% ~03m 19s
|+++++++++++ | 21% ~03m 16s
|++++++++++++ | 22% ~03m 14s
|++++++++++++ | 23% ~03m 11s
|+++++++++++++ | 24% ~03m 08s
|+++++++++++++ | 26% ~03m 05s
|++++++++++++++ | 27% ~03m 06s
|++++++++++++++ | 28% ~03m 03s
|+++++++++++++++ | 29% ~03m 00s
|+++++++++++++++ | 30% ~02m 58s
|++++++++++++++++ | 31% ~02m 55s
|++++++++++++++++ | 32% ~02m 52s
|+++++++++++++++++ | 33% ~02m 49s
|++++++++++++++++++ | 34% ~02m 50s
|++++++++++++++++++ | 35% ~02m 47s
|+++++++++++++++++++ | 36% ~02m 43s
|+++++++++++++++++++ | 37% ~02m 40s
|++++++++++++++++++++ | 38% ~02m 37s
|++++++++++++++++++++ | 39% ~02m 34s
|+++++++++++++++++++++ | 40% ~02m 31s
|+++++++++++++++++++++ | 41% ~02m 29s
|++++++++++++++++++++++ | 43% ~02m 26s
|++++++++++++++++++++++ | 44% ~02m 23s
|+++++++++++++++++++++++ | 45% ~02m 20s
|+++++++++++++++++++++++ | 46% ~02m 17s
|++++++++++++++++++++++++ | 47% ~02m 14s
|++++++++++++++++++++++++ | 48% ~02m 12s
|+++++++++++++++++++++++++ | 49% ~02m 09s
|+++++++++++++++++++++++++ | 50% ~02m 07s
|++++++++++++++++++++++++++ | 51% ~02m 04s
|+++++++++++++++++++++++++++ | 52% ~02m 01s
|+++++++++++++++++++++++++++ | 53% ~01m 58s
|++++++++++++++++++++++++++++ | 54% ~01m 56s
|++++++++++++++++++++++++++++ | 55% ~01m 53s
|+++++++++++++++++++++++++++++ | 56% ~01m 50s
|+++++++++++++++++++++++++++++ | 57% ~01m 48s
|++++++++++++++++++++++++++++++ | 59% ~01m 45s
|++++++++++++++++++++++++++++++ | 60% ~01m 42s
|+++++++++++++++++++++++++++++++ | 61% ~01m 39s
|+++++++++++++++++++++++++++++++ | 62% ~01m 37s
|++++++++++++++++++++++++++++++++ | 63% ~01m 34s
|++++++++++++++++++++++++++++++++ | 64% ~01m 32s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 29s
|+++++++++++++++++++++++++++++++++ | 66% ~01m 26s
|++++++++++++++++++++++++++++++++++ | 67% ~01m 23s
|+++++++++++++++++++++++++++++++++++ | 68% ~01m 21s
|+++++++++++++++++++++++++++++++++++ | 69% ~01m 18s
|++++++++++++++++++++++++++++++++++++ | 70% ~01m 16s
|++++++++++++++++++++++++++++++++++++ | 71% ~01m 13s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01m 10s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01m 07s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01m 05s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01m 02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~59s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~57s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~54s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~52s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~49s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~46s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~43s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~41s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~38s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~35s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~32s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~30s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~27s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~24s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~22s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~19s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~16s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 04m 13s
| | 0 % ~calculating
|+ | 1 % ~03m 06s
|+ | 2 % ~03m 06s
|++ | 3 % ~03m 04s
|++ | 4 % ~03m 02s
|+++ | 5 % ~02m 60s
|+++ | 6 % ~02m 58s
|++++ | 7 % ~02m 56s
|++++ | 8 % ~02m 55s
|+++++ | 9 % ~02m 57s
|+++++ | 10% ~02m 55s
|++++++ | 11% ~02m 52s
|++++++ | 12% ~02m 51s
|+++++++ | 13% ~02m 49s
|+++++++ | 14% ~02m 47s
|++++++++ | 15% ~02m 45s
|++++++++ | 16% ~02m 49s
|+++++++++ | 17% ~02m 47s
|+++++++++ | 18% ~02m 45s
|++++++++++ | 19% ~02m 42s
|++++++++++ | 20% ~02m 40s
|+++++++++++ | 21% ~02m 38s
|+++++++++++ | 22% ~02m 36s
|++++++++++++ | 23% ~02m 34s
|++++++++++++ | 24% ~02m 37s
|+++++++++++++ | 25% ~02m 34s
|+++++++++++++ | 26% ~02m 32s
|++++++++++++++ | 27% ~02m 29s
|++++++++++++++ | 28% ~02m 27s
|+++++++++++++++ | 29% ~02m 24s
|+++++++++++++++ | 30% ~02m 22s
|++++++++++++++++ | 31% ~02m 20s
|++++++++++++++++ | 32% ~02m 17s
|+++++++++++++++++ | 33% ~02m 15s
|+++++++++++++++++ | 34% ~02m 13s
|++++++++++++++++++ | 35% ~02m 11s
|++++++++++++++++++ | 36% ~02m 09s
|+++++++++++++++++++ | 37% ~02m 06s
|+++++++++++++++++++ | 38% ~02m 04s
|++++++++++++++++++++ | 39% ~02m 02s
|++++++++++++++++++++ | 40% ~01m 60s
|+++++++++++++++++++++ | 41% ~01m 58s
|+++++++++++++++++++++ | 42% ~01m 56s
|++++++++++++++++++++++ | 43% ~01m 54s
|++++++++++++++++++++++ | 44% ~01m 52s
|+++++++++++++++++++++++ | 45% ~01m 50s
|+++++++++++++++++++++++ | 46% ~01m 48s
|++++++++++++++++++++++++ | 47% ~01m 46s
|++++++++++++++++++++++++ | 48% ~01m 44s
|+++++++++++++++++++++++++ | 49% ~01m 42s
|+++++++++++++++++++++++++ | 50% ~01m 40s
|++++++++++++++++++++++++++ | 51% ~01m 38s
|++++++++++++++++++++++++++ | 52% ~01m 36s
|+++++++++++++++++++++++++++ | 53% ~01m 34s
|+++++++++++++++++++++++++++ | 54% ~01m 32s
|++++++++++++++++++++++++++++ | 55% ~01m 30s
|++++++++++++++++++++++++++++ | 56% ~01m 28s
|+++++++++++++++++++++++++++++ | 57% ~01m 26s
|+++++++++++++++++++++++++++++ | 58% ~01m 24s
|++++++++++++++++++++++++++++++ | 59% ~01m 22s
|++++++++++++++++++++++++++++++ | 60% ~01m 20s
|+++++++++++++++++++++++++++++++ | 61% ~01m 18s
|+++++++++++++++++++++++++++++++ | 62% ~01m 16s
|++++++++++++++++++++++++++++++++ | 63% ~01m 14s
|++++++++++++++++++++++++++++++++ | 64% ~01m 12s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 10s
|+++++++++++++++++++++++++++++++++ | 66% ~01m 08s
|++++++++++++++++++++++++++++++++++ | 67% ~01m 06s
|++++++++++++++++++++++++++++++++++ | 68% ~01m 04s
|+++++++++++++++++++++++++++++++++++ | 69% ~01m 02s
|+++++++++++++++++++++++++++++++++++ | 70% ~01m 00s
|++++++++++++++++++++++++++++++++++++ | 71% ~58s
|++++++++++++++++++++++++++++++++++++ | 72% ~56s
|+++++++++++++++++++++++++++++++++++++ | 73% ~54s
|+++++++++++++++++++++++++++++++++++++ | 74% ~53s
|++++++++++++++++++++++++++++++++++++++ | 75% ~51s
|++++++++++++++++++++++++++++++++++++++ | 76% ~48s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~46s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~44s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~42s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~40s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~38s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~36s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~34s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~32s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~30s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~28s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~26s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~24s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~22s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~20s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~18s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~16s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~14s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03m 20s
| | 0 % ~calculating
|+ | 1 % ~08m 09s
|++ | 2 % ~08m 05s
|++ | 3 % ~08m 01s
|+++ | 4 % ~08m 11s
|+++ | 5 % ~08m 03s
|++++ | 6 % ~07m 57s
|++++ | 7 % ~08m 09s
|+++++ | 8 % ~08m 01s
|+++++ | 9 % ~07m 53s
|++++++ | 10% ~08m 02s
|++++++ | 11% ~07m 53s
|+++++++ | 12% ~07m 45s
|+++++++ | 13% ~07m 36s
|++++++++ | 14% ~07m 29s
|++++++++ | 15% ~07m 22s
|+++++++++ | 16% ~07m 16s
|+++++++++ | 17% ~07m 09s
|++++++++++ | 18% ~07m 03s
|++++++++++ | 19% ~06m 60s
|+++++++++++ | 20% ~06m 54s
|+++++++++++ | 21% ~06m 48s
|++++++++++++ | 22% ~06m 42s
|++++++++++++ | 23% ~06m 36s
|+++++++++++++ | 24% ~06m 31s
|+++++++++++++ | 25% ~06m 26s
|++++++++++++++ | 26% ~06m 21s
|++++++++++++++ | 27% ~06m 15s
|+++++++++++++++ | 28% ~06m 14s
|+++++++++++++++ | 29% ~06m 08s
|++++++++++++++++ | 30% ~06m 02s
|++++++++++++++++ | 31% ~05m 56s
|+++++++++++++++++ | 32% ~05m 50s
|+++++++++++++++++ | 33% ~05m 44s
|++++++++++++++++++ | 34% ~05m 39s
|++++++++++++++++++ | 35% ~05m 33s
|+++++++++++++++++++ | 36% ~05m 28s
|+++++++++++++++++++ | 37% ~05m 23s
|++++++++++++++++++++ | 38% ~05m 18s
|++++++++++++++++++++ | 39% ~05m 12s
|+++++++++++++++++++++ | 40% ~05m 07s
|+++++++++++++++++++++ | 41% ~05m 01s
|++++++++++++++++++++++ | 42% ~04m 56s
|++++++++++++++++++++++ | 43% ~04m 51s
|+++++++++++++++++++++++ | 44% ~04m 46s
|+++++++++++++++++++++++ | 45% ~04m 40s
|++++++++++++++++++++++++ | 46% ~04m 35s
|++++++++++++++++++++++++ | 47% ~04m 31s
|+++++++++++++++++++++++++ | 48% ~04m 26s
|+++++++++++++++++++++++++ | 49% ~04m 20s
|++++++++++++++++++++++++++ | 51% ~04m 17s
|++++++++++++++++++++++++++ | 52% ~04m 11s
|+++++++++++++++++++++++++++ | 53% ~04m 06s
|+++++++++++++++++++++++++++ | 54% ~04m 00s
|++++++++++++++++++++++++++++ | 55% ~03m 55s
|++++++++++++++++++++++++++++ | 56% ~03m 49s
|+++++++++++++++++++++++++++++ | 57% ~03m 44s
|+++++++++++++++++++++++++++++ | 58% ~03m 39s
|++++++++++++++++++++++++++++++ | 59% ~03m 33s
|++++++++++++++++++++++++++++++ | 60% ~03m 28s
|+++++++++++++++++++++++++++++++ | 61% ~03m 23s
|+++++++++++++++++++++++++++++++ | 62% ~03m 18s
|++++++++++++++++++++++++++++++++ | 63% ~03m 13s
|++++++++++++++++++++++++++++++++ | 64% ~03m 07s
|+++++++++++++++++++++++++++++++++ | 65% ~03m 02s
|+++++++++++++++++++++++++++++++++ | 66% ~02m 57s
|++++++++++++++++++++++++++++++++++ | 67% ~02m 52s
|++++++++++++++++++++++++++++++++++ | 68% ~02m 46s
|+++++++++++++++++++++++++++++++++++ | 69% ~02m 42s
|+++++++++++++++++++++++++++++++++++ | 70% ~02m 36s
|++++++++++++++++++++++++++++++++++++ | 71% ~02m 31s
|++++++++++++++++++++++++++++++++++++ | 72% ~02m 26s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02m 21s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02m 16s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02m 11s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02m 05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01m 60s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01m 55s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 49s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01m 44s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01m 39s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01m 34s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 29s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01m 23s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01m 18s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01m 13s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01m 08s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01m 02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~57s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~52s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~47s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~42s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~36s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~31s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~26s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~21s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~16s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 08m 32s
| | 0 % ~calculating
|+ | 1 % ~03m 27s
|++ | 2 % ~03m 26s
|++ | 3 % ~03m 24s
|+++ | 4 % ~03m 29s
|+++ | 5 % ~03m 25s
|++++ | 6 % ~03m 22s
|++++ | 7 % ~03m 19s
|+++++ | 8 % ~03m 16s
|+++++ | 9 % ~03m 14s
|++++++ | 11% ~03m 11s
|++++++ | 12% ~03m 09s
|+++++++ | 13% ~03m 06s
|+++++++ | 14% ~03m 04s
|++++++++ | 15% ~03m 01s
|++++++++ | 16% ~03m 02s
|+++++++++ | 17% ~02m 60s
|+++++++++ | 18% ~02m 58s
|++++++++++ | 19% ~02m 55s
|++++++++++ | 20% ~02m 53s
|+++++++++++ | 21% ~02m 50s
|++++++++++++ | 22% ~02m 48s
|++++++++++++ | 23% ~02m 49s
|+++++++++++++ | 24% ~02m 47s
|+++++++++++++ | 25% ~02m 44s
|++++++++++++++ | 26% ~02m 42s
|++++++++++++++ | 27% ~02m 40s
|+++++++++++++++ | 28% ~02m 37s
|+++++++++++++++ | 29% ~02m 34s
|++++++++++++++++ | 31% ~02m 36s
|++++++++++++++++ | 32% ~02m 33s
|+++++++++++++++++ | 33% ~02m 30s
|+++++++++++++++++ | 34% ~02m 28s
|++++++++++++++++++ | 35% ~02m 25s
|++++++++++++++++++ | 36% ~02m 22s
|+++++++++++++++++++ | 37% ~02m 20s
|+++++++++++++++++++ | 38% ~02m 17s
|++++++++++++++++++++ | 39% ~02m 14s
|++++++++++++++++++++ | 40% ~02m 12s
|+++++++++++++++++++++ | 41% ~02m 09s
|++++++++++++++++++++++ | 42% ~02m 07s
|++++++++++++++++++++++ | 43% ~02m 04s
|+++++++++++++++++++++++ | 44% ~02m 02s
|+++++++++++++++++++++++ | 45% ~01m 60s
|++++++++++++++++++++++++ | 46% ~01m 57s
|++++++++++++++++++++++++ | 47% ~01m 55s
|+++++++++++++++++++++++++ | 48% ~01m 53s
|+++++++++++++++++++++++++ | 49% ~01m 51s
|++++++++++++++++++++++++++ | 51% ~01m 48s
|++++++++++++++++++++++++++ | 52% ~01m 46s
|+++++++++++++++++++++++++++ | 53% ~01m 44s
|+++++++++++++++++++++++++++ | 54% ~01m 41s
|++++++++++++++++++++++++++++ | 55% ~01m 39s
|++++++++++++++++++++++++++++ | 56% ~01m 36s
|+++++++++++++++++++++++++++++ | 57% ~01m 34s
|+++++++++++++++++++++++++++++ | 58% ~01m 32s
|++++++++++++++++++++++++++++++ | 59% ~01m 29s
|++++++++++++++++++++++++++++++ | 60% ~01m 27s
|+++++++++++++++++++++++++++++++ | 61% ~01m 25s
|++++++++++++++++++++++++++++++++ | 62% ~01m 23s
|++++++++++++++++++++++++++++++++ | 63% ~01m 20s
|+++++++++++++++++++++++++++++++++ | 64% ~01m 18s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 16s
|++++++++++++++++++++++++++++++++++ | 66% ~01m 13s
|++++++++++++++++++++++++++++++++++ | 67% ~01m 12s
|+++++++++++++++++++++++++++++++++++ | 68% ~01m 10s
|+++++++++++++++++++++++++++++++++++ | 69% ~01m 07s
|++++++++++++++++++++++++++++++++++++ | 71% ~01m 05s
|++++++++++++++++++++++++++++++++++++ | 72% ~01m 02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01m 00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~58s
|++++++++++++++++++++++++++++++++++++++ | 75% ~55s
|++++++++++++++++++++++++++++++++++++++ | 76% ~53s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~51s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~48s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~46s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~44s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~41s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~39s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~37s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~34s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~32s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~30s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~28s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~25s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~23s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~21s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~18s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~16s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03m 36swrite.table(GA2123wk_v3.specific.type.markers,"GA2123wk_v3.specificType.markers.txt",sep="\t")GA2123wk_v3.specific.clust.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)DoHeatmap(object = Hs_GA2123_Trachea_v3, genes.use = c("CFTR","ANO1","EPCAM","TP63","KRT5","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","MUC16","MUC1","MUC4","MUC20","SERPINB3","CD9","KRT14","SOSTDC1","MUC5B","SPDEF","RNASE1","LYZ","SNAP25","ASCL1","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","ACTA2","RGS5","NOTCH3","SOX9","COL2A1","ACAN","WNT2","THY1","TWIST2","MKI67"),
slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="specific_type",group.cex = 30,cex.row=30,group.order = c("Basal_SE","Ciliated_Foxn4","Ciliated","Secretory_SE","Basal_SMG","Secretory_SMG","Myoepithelial","Epcam_ECM","Stem","Schwann/Neural","Immune","VascularEndothelial","LymphaticEndothelial","Muscle","Chondrocyte","MesenchymalProgenitor","Fibroblast","CyclingFibroblast")
)
print(levels(Hs_GA2123_Trachea_v3@ident))Hs_GA2123_Trachea_v3<-SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "specific_type")
Hs_GA2123_Trachea_v3@ident = factor(Hs_GA2123_Trachea_v3@ident,levels(Hs_GA2123_Trachea_v3@ident)[c(1,15,5,4,2,16,13,7,17,14,9,10,18,12,3,11,8,6)])
DotPlot(object = Hs_GA2123_Trachea_v3, cols.use = c("lightgray","red"),genes.plot = c("CFTR","ANO1","EPCAM","TP63","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","MUC16","SERPINB3","SOX9","KRT14","SOSTDC1","MUC5B","SPDEF","LTF","LYZ","ACTA2","POU5F1","ESRG","SNAP25","CHGA","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","MYH11","RGS5","NOTCH3","COL2A1","ACAN","WNT2","CD34","THY1","TWIST2","MKI67"),group.by = "ident", x.lab.rot = T,plot.legend = T)DotPlot(object = Hs_GA2123_Trachea_v3, cols.use = c("forestgreen","magenta3"),genes.plot = c("CFTR","ANO1","EPCAM","TP63","KRT5","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","MUC16","SERPINB3","SOX9","KRT14","SOSTDC1","MUC5B","MUC5AC","SPDEF","LTF","LYZ","ACTA2","POU5F1","ESRG","SNAP25","CHGA","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","MYH11","RGS5","NOTCH3","COL2A1","ACAN","WNT2","PI16","CD34","THY1","TWIST2","MKI67"),group.by = "ident", x.lab.rot = T,plot.legend = T,col.min = -2,col.max = 2)DotPlot(object = Hs_GA2123_Trachea_v3, cols.use = c("forestgreen","magenta3"),genes.plot = c("CFTR","ANO1","EPCAM","TP63","KRT5","KRT14","SOSTDC1","SOX9","FOXN4","SHISA8","SNTN","FOXJ1","TFF3","MUC5B","SPDEF","LTF","LYZ","ACTA2","POU5F1","ESRG","TUBB3","SNAP25","CHGA","ASCL1","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","MYH11","RGS5","NOTCH3","COL2A1","ACAN","WNT2","CD34","THY1","CLEC3B","TWIST2","MKI67"),group.by = "ident", x.lab.rot = T,plot.legend = T,col.min = -2,col.max = 2)DotPlot(object = Hs_GA2123_Trachea_v3, cols.use = c("forestgreen","magenta3"),genes.plot = c("FOXJ1","LTF","TP63","WNT2","PI16","CLEC3B","EPCAM","TERC","TERT","CLDN6","POU5F1","LIN28A","ESRG","L1TD1","DPPA4","UTF1","FOXD3-AS1","CRABP1","THY1","TUBB2B","UCHL1","TUBB3","SNAP25","PLP1"),group.by = "ident", x.lab.rot = T,plot.legend = T)df_Hs<-FetchData(Hs_GA2123_Trachea_v3,c("ANO1","CFTR","SERPINB3","MUC16","specific_type"))
ggplot(df_Hs,aes(specific_type,CFTR))+geom_dotplot(binaxis="y",aes(fill=specific_type),binwidth=0.05,stackdir="center",position=position_dodge(0.8), dotsize=0.018)+ theme(axis.text.x = element_text(angle = 45,hjust=1))+ stat_summary(aes(color=specific_type),fun.data=mean_sdl, fun.args = list(mult=1),
geom="pointrange",position=position_dodge(0.7))TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = F,group.by="specific_type",pt.size = 0.3)+scale_color_manual(values=c('#e6194b' , '#808080','#3cb44b', '#ffe119', '#4363d8', '#911eb4', '#46f0f0', '#f032e6', '#bcf60c', '#008080', '#e6beff', '#9a6324', '#fabebe', '#800000', '#aaffc3', '#808000','#ffd8b1', '#000075', '#f58231', '#000000','#fffac8'
))
For the purpose of visualization, we average within each specific cell type:
Hs_GA2123_Trachea_v3<-SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "specific_type")
average_Hs_specific_Annotation<-AverageExpression(object = Hs_GA2123_Trachea_v3,return.seurat = T)
DoHeatmap(object = average_Hs_specific_Annotation, genes.use = c("EPCAM","TP63","KRT5","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","MUC16","SERPINB3","SOX9","KRT14","SOSTDC1","MUC5B","MUC5AC","SPDEF","LTF","LYZ","ACTA2","MYH11","POU5F1","ESRG","SNAP25","ASCL1","CHGA","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","RGS5","NOTCH3","COL2A1","ACAN","WNT2","PI16","CD34","THY1","TWIST2","MKI67","CFTR","ANO1"),
slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.cex = 30,cex.row=20,group.order = c("Basal_SE","Basal_SMG","Ciliated_Foxn4","Ciliated","Secretory_SE",
"Secretory_SMG", "Myoepithelial","Epcam_ECM","Stem","Schwann/Neural","Immune","LymphaticEndothelial","VascularEndothelial","Muscle","Chondrocyte","MesenchymalProgenitor","Fibroblast","CyclingFibroblast"))
DoHeatmap(object = average_Hs_specific_Annotation, genes.use = c("EPCAM","TP63","KRT5","KRT14","SOSTDC1","SOX9","FOXN4","SHISA8","SNTN","FOXJ1","TFF3","MUC5B","SPDEF","LTF","LYZ","ACTA2","POU5F1","ESRG","TUBB3","SNAP25","CHGA","ASCL1","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","MYH11","RGS5","NOTCH3","COL2A1","ACAN","WNT2","CD34","THY1","CLEC3B","TWIST2","MKI67","CFTR","ANO1"),
slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.cex = 30,cex.row=20,group.order = c("Basal_SE","Basal_SMG","Ciliated_Foxn4","Ciliated","Secretory_SE",
"Secretory_SMG", "Myoepithelial","Epcam_ECM","Stem","Schwann/Neural","Immune","LymphaticEndothelial","VascularEndothelial","Muscle","Chondrocyte","MesenchymalProgenitor","Fibroblast","CyclingFibroblast"))save(Hs_GA2123_Trachea_v3_sub1,file="seurat_GA2123wk_v3_sub1.RData")save(Hs_GA2123_Trachea_v3,file="seurat_GA2123wk_v3.RData")check a few virus receptors:
VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("CDHR3","ACE2","TMPRSS2","TMPRSS4","ANPEP","DPP4","ST6GAL1","ST3GAL4"), nCol = 2,group.by="specific_type",point.size.use=0.3,x.lab.rot = T)
---
title: "Hs_Apr3"
output: html_notebook
---

#### Human fetal trachea samples collected on Apr3 2019. 10X genomics 3' kit, v3 chemistry. 
#### The samples come from 2 individuals of gestational ages of 21 weeks and 23 weeks respectively.

```{r}
library(Seurat)
library(dplyr)
```
```{r}
ZipF<-list.files(path=".",pattern="*.gz",full.names = T,recursive = T)
ZipF
```
```{r}
library(plyr)
library(R.utils)
ldply(.data=ZipF, .fun=gunzip)  #This just unzips locally
```

```{r}
##### First I manually changed all featurres.tsv to genes.tsv. Otherwise Read10X (Seurat v2) would not recognize.
# Load data
file_10Xdir_Hs<-c("GA21wk_v3","GA23wk_v3")
names(file_10Xdir_Hs)<-c("GA21wk_v3","GA23wk_v3")
Hs_Apr3_v3.data <- Read10X(data.dir = file_10Xdir_Hs)
```

```{r}
dim(Hs_Apr3_v3.data)
```

##### 26577 genes based on HG38-plus reference
##### 38892 "cells" are identified by Cell Ranger

```{r}
Hs_GA2123_Trachea_v3 <- CreateSeuratObject(raw.data = Hs_Apr3_v3.data, min.cells = 1, min.genes = 1, 
    project = "Hs_GA2123_Trachea_v3chemistry")
Hs_GA2123_Trachea_v3@raw.data@Dim
```
```{r}
head(Hs_GA2123_Trachea_v3@cell.names)
```

```{r}
Hs_GA2123_Trachea_v3 <- FilterCells(object = Hs_GA2123_Trachea_v3, subset.names = c("nGene","nUMI"), 
    low.thresholds = c(1000,4000), high.thresholds = c(Inf,Inf))
Hs_GA2123_Trachea_v3@data@Dim
```

```{r}
cell_name<-read.table(text=Hs_GA2123_Trachea_v3@cell.names,sep="_",colClasses = "character")
age<-cell_name[,1]
names(age)<-Hs_GA2123_Trachea_v3@cell.names
```
```{r}
Hs_GA2123_Trachea_v3<-AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = age, col.name = "age")
```

```{r}
table(Hs_GA2123_Trachea_v3@meta.data$age)
```

```{r}
ribo.genes <- grep(pattern = "^RP[SL][[:digit:]]", x = rownames(x = Hs_GA2123_Trachea_v3@data), value = TRUE)
percent.ribo <- Matrix::colSums(Hs_GA2123_Trachea_v3@raw.data[ribo.genes, ])/Matrix::colSums(Hs_GA2123_Trachea_v3@raw.data)
Hs_GA2123_Trachea_v3 <- AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = percent.ribo, col.name = "percent.ribo")
```

```{r}
aggregate(Hs_GA2123_Trachea_v3@meta.data[, c(1:2,5)], list(Hs_GA2123_Trachea_v3@meta.data$age), median)

```

```{r}
Hs_GA2123_Trachea_v3 <- NormalizeData(object = Hs_GA2123_Trachea_v3)
```

```{r}
Hs_GA2123_Trachea_v3 <- ScaleData(object = Hs_GA2123_Trachea_v3)
```

```{r}
Hs_GA2123_Trachea_v3 <- FindVariableGenes(object = Hs_GA2123_Trachea_v3, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
```

```{r}
Hs_GA2123_Trachea_v3 <- RunPCA(object = Hs_GA2123_Trachea_v3, do.print = FALSE)
Hs_GA2123_Trachea_v3 <- ProjectPCA(object = Hs_GA2123_Trachea_v3, do.print = FALSE)
```
```{r,fig.height=50,fig.width=15}
PCHeatmap(object = Hs_GA2123_Trachea_v3, pc.use = 1:10, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)

```

```{r}
PCElbowPlot(object = Hs_GA2123_Trachea_v3)
```

```{r}
n.pcs = 20
res.used <- 0.8

Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```
```{r}
Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2)

```

```{r}
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.0.8",pt.size = 0.2)

```

```{r}
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = F,group.by="age",pt.size = 0.1)
```

```{r}
n.pcs = 20
res.used <- 1.0

Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE,force.recalc=T)
```
```{r}
Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

```

```{r}
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1")

```

```{r,fig.height=15,fig.width=60}
DoHeatmap(object = Hs_GA2123_Trachea_v3, genes.use = c("ANO1","CFTR","EPCAM","TP63","FOXJ1","FOXN4","SCGB1A1","LTF","SNAP25","ASCL1","CHGA","PLP1","MPZ","SOX10","C1QA","FCER1G","PECAM1","LYVE1","RGS5","NOTCH3","ACTA2","ACTG2","DES","PDLIM3","FGL2","PCDH7","MYH11","COL11A1","SOX9","SOX5","SOX6","COL2A1","ACAN","SERPINF1","COL1A1","THBS2","KERA","DCN","LUM","CD34","WNT2","THY1","PI16","CLEC3B","MKI67","TOP2A","TWIST2"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1",group.cex = 25,cex.row=25,group.order = c(9,16,12,14,10,19,3,22,20,21,5,7,15,17,8,18,4,0,2,1,13,11,6)
  )
```

```{r}
Hs_GA2123_Trachea_v3<-SetAllIdent(Hs_GA2123_Trachea_v3,id="res.1")
GA2123wk_v3.res1.clust.markers <- FindAllMarkers(object = Hs_GA2123_Trachea_v3, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)

```

```{r}
GA2123wk_v3.res1.clust.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)
```
```{r}
write.table(GA2123wk_v3.res1.clust.markers,"GA2123wk_v3.res1.markers.txt",sep="\t")

```
```{r}
Hs_v3_res1_8_18<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(8),ident.2=c(18),only.pos = F)
Hs_v3_res1_8_18
```

```{r}
Hs_v3_res1_2over1<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(2),ident.2=c(1),only.pos = T)
Hs_v3_res1_2over1
```

```{r}
Hs_v3_res1_21_20<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(21),ident.2=c(20),only.pos = T)
Hs_v3_res1_21_20
```

```{r}
n.pcs = 20
res.used <- 1.2

Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```

```{r}
Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

```

```{r}
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1.2")

```

```{r}
n.pcs = 20
res.used <- 1.4

Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```

```{r}
Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

```

```{r}
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1.4")

```

##### inport doublet scores generated by Scrublet, and make them a metadata column:

```{r}
load("GA2123wk_apr3_v3_doubletScore.RData")
Hs_GA2123_Trachea_v3<-AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = GA2123wk_apr3_v3_doubletScore, col.name = "doublet_score")
sum(is.na(Hs_GA2123_Trachea_v3@meta.data$doublet_score))
```


```{r,fig.height=5, fig.width=20}
Hs_GA2123_Trachea_v3<-SetAllIdent(Hs_GA2123_Trachea_v3,id="age")
VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("doublet_score"), nCol = 1,group.by="res.1.4",point.size.use=0.3,ident.include = "GA21wk")
```

```{r,fig.height=8, fig.width=20}
VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("doublet_score"), nCol = 1,group.by="res.1.4",point.size.use=0.3,ident.include = "GA23wk")
```


```{r,fig.height=20,fig.width=60}
DoHeatmap(object = Hs_GA2123_Trachea_v3, genes.use = c("ANO1","CFTR","EPCAM","KRT8","KRT18","TP63","KRT5","KRT14","SOSTDC1","KRT4","KRT13","SPDEF","CREB3L1","MUC5B","FOXJ1","FOXN4","SHISA8","MCIDAS","TUBB3","SNAP25","ASCL1","CHGA","PLP1","MPZ","C1QA","FCER1G","CD3G","PECAM1","NRP1","LYVE1","RGS5","NOTCH3","ACTA2","TAGLN","MYH11","COL8A1","COL11A1","SOX9","COL2A1","ACAN","MIA","DCN","LUM","CD34","WNT2","THY1","PI16","CLEC3B","TK1","MKI67","TOP2A","ALAS2"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 35,cex.row=25,group.order = c(8,14,17,19,10,21,9,11,24,22,23,3,5,16,18,6,20,13,7,0,1,2,15,12,4)
  )
```



```{r,fig.height=15, fig.width=20}
VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("SOX10","PHOX2A", "PHOX2B","CHGA","ASCL1","RET"), nCol = 1,group.by="res.1.4",point.size.use=0.3)
```
```{r,fig.height=8,fig.width=40}
    DoHeatmap(object = Hs_GA2123_Trachea_v3, genes.use = c("EPCAM","TUBB3","SNAP25","ASCL1","CHGA","PHOX2A","PHOX2B","PLP1","MPZ"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 35,cex.row=25,cells.use = Hs_GA2123_Trachea_v3@cell.names[Hs_GA2123_Trachea_v3@meta.data$res.1.4 %in% c(10)]
  )
```


##### subset the Non-EPCAM cells:
```{r}
Hs_GA2123_Trachea_v3 <- SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "res.1.4")
Hs_GA2123_Trachea_v3_nonEpcam<-SubsetData(object=Hs_GA2123_Trachea_v3,ident.use=c(0:7,9:13,15,16,18,20:24))
table(Hs_GA2123_Trachea_v3_nonEpcam@meta.data$res.1.4)
```

```{r}
colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.0.8'] <- 'orig.0.8'
colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.1.4'] <- 'orig.1.4'
colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.1.2'] <- 'orig.1.2'
```

```{r}
Hs_GA2123_Trachea_v3_nonEpcam <- ScaleData(object = Hs_GA2123_Trachea_v3_nonEpcam)
```

```{r}
Hs_GA2123_Trachea_v3_nonEpcam <- FindVariableGenes(object = Hs_GA2123_Trachea_v3_nonEpcam, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
```


######run PCA on the set of genes

```{r}
Hs_GA2123_Trachea_v3_nonEpcam <- RunPCA(object = Hs_GA2123_Trachea_v3_nonEpcam, do.print = FALSE)
#PCAPlot(Hs_GA2123_Trachea_v3_nonEpcam)
```

```{r}
Hs_GA2123_Trachea_v3_nonEpcam <- ProjectPCA(object = Hs_GA2123_Trachea_v3_nonEpcam, do.print = F)
```

```{r}
PCElbowPlot(object = Hs_GA2123_Trachea_v3_nonEpcam)
```
```{r,fig.height=30,fig.width=15}
PCHeatmap(object = Hs_GA2123_Trachea_v3_nonEpcam, pc.use = 1:20, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)

```

```{r}
n.pcs = 20
res.used <- 0.8

Hs_GA2123_Trachea_v3_nonEpcam <- FindClusters(object = Hs_GA2123_Trachea_v3_nonEpcam, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE,force.recalc = T)
```

```{r}
Hs_GA2123_Trachea_v3_nonEpcam <- RunTSNE(object = Hs_GA2123_Trachea_v3_nonEpcam, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

```

```{r}
TSNEPlot(object = Hs_GA2123_Trachea_v3_nonEpcam, do.label = T,group.by="res.0.8")

```


```{r,fig.height=20,fig.width=60}
DoHeatmap(object = Hs_GA2123_Trachea_v3_nonEpcam, genes.use = c("ANO1","CFTR","TUBB3","SNAP25","ASCL1","CHGA","PLP1","MPZ","C1QA","FCER1G","CD3G","PECAM1","NRP1","LYVE1","RGS5","NOTCH3","ACTA2","TAGLN","MYH11","COL8A1","COL11A1","SOX9","COL2A1","ACAN","MIA","DCN","LUM","CD34","WNT2","THY1","PI16","CLEC3B","TK1","MKI67","TOP2A","ADIPOQ","CAR3"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.0.8",group.cex = 35,cex.row=25,group.order = c(9,14,2,17,15,16,13,6,8,5,4,0,3,1,10,11,12,7)
  )
```

```{r}
table(Hs_GA2123_Trachea_v3_nonEpcam@meta.data$orig.1.4,Hs_GA2123_Trachea_v3_nonEpcam@meta.data$res.0.8)
```


```{r}
library(ggalluvial)
```
```{r, fig.height=9, fig.width=6}
ggplot(data=Hs_GA2123_Trachea_v3_nonEpcam@meta.data,aes(axis1=orig.1.4,axis2=res.0.8))+geom_alluvium(aes(fill=res.0.8))+geom_stratum(width = 1/12, fill = "black", color = "grey") +geom_label(stat = "stratum", label.strata = TRUE)+scale_x_discrete(limits = c("orig.1.4", "res.0.8"), expand = c(.05, .05))
```


##### Now subset the basal, ciliated, and secretory:

```{r}
Hs_GA2123_Trachea_v3 <- SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "res.1.4")
Hs_GA2123_Trachea_v3_sub1<-SubsetData(object=Hs_GA2123_Trachea_v3,ident.use=c(8,14,19))
table(Hs_GA2123_Trachea_v3_sub1@meta.data$res.1.4)
```

```{r}
colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.0.8'] <- 'orig.0.8'
colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.1.4'] <- 'orig.1.4'
colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.1.2'] <- 'orig.1.2'
```

```{r}
Hs_GA2123_Trachea_v3_sub1 <- ScaleData(object = Hs_GA2123_Trachea_v3_sub1)
```

```{r}
Hs_GA2123_Trachea_v3_sub1 <- FindVariableGenes(object = Hs_GA2123_Trachea_v3_sub1, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
```

######run PCA on the set of genes

```{r}
Hs_GA2123_Trachea_v3_sub1 <- RunPCA(object = Hs_GA2123_Trachea_v3_sub1, do.print = FALSE)
#PCAPlot(Hs_GA2123_Trachea_v3_sub1)
```

```{r}
Hs_GA2123_Trachea_v3_sub1 <- ProjectPCA(object = Hs_GA2123_Trachea_v3_sub1, do.print = F)
```

```{r}
PCElbowPlot(object = Hs_GA2123_Trachea_v3_sub1)
```
```{r,fig.height=30,fig.width=15}
PCHeatmap(object = Hs_GA2123_Trachea_v3_sub1, pc.use = 1:12, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)

```

```{r}
n.pcs = 16
res.used <- 0.8

Hs_GA2123_Trachea_v3_sub1 <- FindClusters(object = Hs_GA2123_Trachea_v3_sub1, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```
```{r}
Hs_GA2123_Trachea_v3_sub1 <- RunTSNE(object = Hs_GA2123_Trachea_v3_sub1, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

```

```{r}
TSNEPlot(object = Hs_GA2123_Trachea_v3_sub1, do.label = T)

```


```{r,fig.height=20,fig.width=60}
DoHeatmap(object = Hs_GA2123_Trachea_v3_sub1, genes.use = c("TP63","KRT15","KRT5","KRT17","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","KRT4","MUC1","MUC4","MUC20","SERPINB3","GSTP1","ALOX15","CD9","MYH11","ACTG2","MYLK","TAGLN","LTF","AZGP1","DMBT1","FCGBP","CCL28","AQP5","MUC5B","SPDEF","RNASE1","LYZ","TIMP3","OGN","COL14A1","BGN","COL11A1","LUM","ACAN","CFTR","ANO1","TACSTD2"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.0.8",group.cex = 60,cex.row=30,group.order = c(4,2,6,1,7,5,0,3)
  )
```

```{r,fig.height=4,fig.width=16}
DotPlot(object = Hs_GA2123_Trachea_v3_sub1, cols.use = c("forestgreen","magenta3"),genes.plot = c("TP63","KRT15","KRT5","KRT17","KRT14","SOSTDC1","FOXJ1","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","CFAP53","CETN2","KRT4","KRT13","MUC1","MUC4","MUC16","MUC20","SERPINB3","MYH11","ACTG2","MYLK","APOE","TAGLN","LTF","AZGP1","DMBT1","KCNN4","FCGBP","LRRC26","KRT7","CCL28","AQP5","MUC5B","SPDEF","LYZ","TIMP3","OGN","COL14A1","BGN","MGP","COL11A1","LUM","ACAN","CFTR","ANO1"),group.by = "ident", x.lab.rot = T,plot.legend = T)
```

```{r}
prop.table(table(Hs_GA2123_Trachea_v3_sub1@meta.data$age,Hs_GA2123_Trachea_v3_sub1@meta.data$res.0.8),1)

```

```{r}
n.pcs = 16
res.used <- 1.2

Hs_GA2123_Trachea_v3_sub1 <- FindClusters(object = Hs_GA2123_Trachea_v3_sub1, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```

```{r}
Hs_GA2123_Trachea_v3_sub1 <- RunTSNE(object = Hs_GA2123_Trachea_v3_sub1, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

```

```{r}
TSNEPlot(object = Hs_GA2123_Trachea_v3_sub1, do.label = T,group.by="res.1.2")

```

```{r,fig.height=20,fig.width=60}
DoHeatmap(object = Hs_GA2123_Trachea_v3_sub1, genes.use = c("TP63","KRT15","KRT5","KRT17","KRT14","SOSTDC1","SMOC2","SOX9","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","KRT4","MUC1","MUC4","MUC20","SERPINB3","GSTP1","ALOX15","CD9","MYH11","ACTG2","MYLK","TAGLN","LTF","AZGP1","DMBT1","FCGBP","CCL28","AQP5","MUC5B","SPDEF","RNASE1","LYZ","TIMP3","OGN","COL14A1","BGN","COL11A1","LUM","ACAN","CFTR","ANO1","TACSTD2","SERPINB4","SERPINB13","NPPC"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.2",group.cex = 60,cex.row=30,group.order = c(2,4,6,1,7,8,5,0,3)
  )
```


```{r}
Hs_v3_sub1_res1.2_c8over2_4<-FindMarkers(Hs_GA2123_Trachea_v3_sub1,ident.1=c(8),ident.2 = c(2,4),only.pos = TRUE)
Hs_v3_sub1_res1.2_c8over2_4
```

```{r}
library(plyr)
Hs_GA2123_Trachea_v3_sub1@meta.data$cell_type<-mapvalues(Hs_GA2123_Trachea_v3_sub1@meta.data$res.1.2,from=c("0","1","2","3","4","5","6","7","8"),to=c("Secretory_SMG","Ciliated","Basal_SE","Epcam_ECM","Basal_SE","Myoepithelial","Ciliated_Foxn4","Secretory_SE","Basal_SMG"))
```


```{r,fig.height=30,fig.width=60}
DoHeatmap(object = Hs_GA2123_Trachea_v3_sub1, genes.use = c("FOXN4","PLK4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","TP63","KRT15","KRT5","KRT17","KRT14","SOSTDC1","SMOC2","NPPC","KRT4","MUC1","MUC4","MUC20","SERPINB3","SERPINB4","SERPINB13","GSTP1","ALOX15","CD9","SOX9","LTF","AQP5","LRRC26","AZGP1","DMBT1","FCGBP","CCL28","MUC5B","SPDEF","RNASE1","LYZ","MYH11","ACTG2","MYLK","TAGLN","TIMP3","OGN","COL14A1","BGN","COL11A1","LUM","ACAN","CFTR","ANO1"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="cell_type",group.cex = 60,cex.row=30,group.order = c("Ciliated_Foxn4","Ciliated","Basal_SE","Secretory_SE","Basal_SMG","Secretory_SMG","Myoepithelial","Epcam_ECM")
  )
```

##### To annotate Hs_GA2123_Trachea_v3:

```{r}
Hs_v3_type_sub1<-Hs_GA2123_Trachea_v3_sub1@meta.data$cell_type
names(Hs_v3_type_sub1)<-Hs_GA2123_Trachea_v3_sub1@cell.names
```

```{r}
Hs_GA2123_Trachea_v3@meta.data$cell_type<-mapvalues(Hs_GA2123_Trachea_v3@meta.data$res.1,from=c("0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22"),to=c("Fibroblast","Fibroblast","Fibroblast","VascularEndothelial","Fibroblast","Fibroblast","CyclingFibroblast","Fibroblast","Chondrocyte","Basal","Schwann/Neural","Fibroblast","Secretory","MesenchymalProgenitor","Stem","Fibroblast","Ciliated","Fibroblast","Chondrocyte","Immune","Muscle","Muscle","LymphaticEndothelial"))
```

#####use the Hs_GA2123_Trachea_v3_sub1 information to further annotate Hs_GA2123_Trachea_v3:

```{r}
Hs_GA2123_Trachea_v3<-AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = Hs_v3_type_sub1, col.name = "specific_type")
```
```{r}
table(Hs_GA2123_Trachea_v3@meta.data$specific_type)
```

```{r}
Hs_GA2123_Trachea_v3@meta.data$specific_type <- ifelse(is.na(Hs_GA2123_Trachea_v3@meta.data$specific_type), as.character(Hs_GA2123_Trachea_v3@meta.data$cell_type), as.character(Hs_GA2123_Trachea_v3@meta.data$specific_type))
```

##### now we have annotation for all cells:

```{r}
table(Hs_GA2123_Trachea_v3@meta.data$specific_type,Hs_GA2123_Trachea_v3@meta.data$age)
```
```{r}
Hs_GA2123_Trachea_v3<-SetAllIdent(Hs_GA2123_Trachea_v3,id="specific_type")
GA2123wk_v3.specific.type.markers <- FindAllMarkers(object = Hs_GA2123_Trachea_v3, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
write.table(GA2123wk_v3.specific.type.markers,"GA2123wk_v3.specificType.markers.txt",sep="\t")

```

```{r}
GA2123wk_v3.specific.clust.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)
```
```{r}

```


```{r,fig.height=20,fig.width=60}
DoHeatmap(object = Hs_GA2123_Trachea_v3, genes.use = c("CFTR","ANO1","EPCAM","TP63","KRT5","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","MUC16","MUC1","MUC4","MUC20","SERPINB3","CD9","KRT14","SOSTDC1","MUC5B","SPDEF","RNASE1","LYZ","SNAP25","ASCL1","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","ACTA2","RGS5","NOTCH3","SOX9","COL2A1","ACAN","WNT2","THY1","TWIST2","MKI67"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="specific_type",group.cex = 30,cex.row=30,group.order = c("Basal_SE","Ciliated_Foxn4","Ciliated","Secretory_SE","Basal_SMG","Secretory_SMG","Myoepithelial","Epcam_ECM","Stem","Schwann/Neural","Immune","VascularEndothelial","LymphaticEndothelial","Muscle","Chondrocyte","MesenchymalProgenitor","Fibroblast","CyclingFibroblast")
  )
```

```{r}
print(levels(Hs_GA2123_Trachea_v3@ident))
```

```{r,fig.height=6,fig.width=12}
Hs_GA2123_Trachea_v3<-SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "specific_type")
Hs_GA2123_Trachea_v3@ident = factor(Hs_GA2123_Trachea_v3@ident,levels(Hs_GA2123_Trachea_v3@ident)[c(1,15,5,4,2,16,13,7,17,14,9,10,18,12,3,11,8,6)])

```

```{r,fig.height=5,fig.width=12}
DotPlot(object = Hs_GA2123_Trachea_v3, cols.use = c("lightgray","red"),genes.plot = c("CFTR","ANO1","EPCAM","TP63","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","MUC16","SERPINB3","SOX9","KRT14","SOSTDC1","MUC5B","SPDEF","LTF","LYZ","ACTA2","POU5F1","ESRG","SNAP25","CHGA","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","MYH11","RGS5","NOTCH3","COL2A1","ACAN","WNT2","CD34","THY1","TWIST2","MKI67"),group.by = "ident", x.lab.rot = T,plot.legend = T)
```

```{r,fig.height=5,fig.width=12}
DotPlot(object = Hs_GA2123_Trachea_v3, cols.use = c("forestgreen","magenta3"),genes.plot = c("CFTR","ANO1","EPCAM","TP63","KRT5","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","MUC16","SERPINB3","SOX9","KRT14","SOSTDC1","MUC5B","MUC5AC","SPDEF","LTF","LYZ","ACTA2","POU5F1","ESRG","SNAP25","CHGA","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","MYH11","RGS5","NOTCH3","COL2A1","ACAN","WNT2","PI16","CD34","THY1","TWIST2","MKI67"),group.by = "ident", x.lab.rot = T,plot.legend = T,col.min = -2,col.max = 2)
```
```{r,fig.height=5,fig.width=12}
DotPlot(object = Hs_GA2123_Trachea_v3, cols.use = c("forestgreen","magenta3"),genes.plot = c("CFTR","ANO1","EPCAM","TP63","KRT5","KRT14","SOSTDC1","SOX9","FOXN4","SHISA8","SNTN","FOXJ1","TFF3","MUC5B","SPDEF","LTF","LYZ","ACTA2","POU5F1","ESRG","TUBB3","SNAP25","CHGA","ASCL1","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","MYH11","RGS5","NOTCH3","COL2A1","ACAN","WNT2","CD34","THY1","CLEC3B","TWIST2","MKI67"),group.by = "ident", x.lab.rot = T,plot.legend = T,col.min = -2,col.max = 2)
```
```{r,fig.height=6,fig.width=8}
DotPlot(object = Hs_GA2123_Trachea_v3, cols.use = c("forestgreen","magenta3"),genes.plot = c("FOXJ1","LTF","TP63","WNT2","PI16","CLEC3B","EPCAM","TERC","TERT","CLDN6","POU5F1","LIN28A","ESRG","L1TD1","DPPA4","UTF1","FOXD3-AS1","CRABP1","THY1","TUBB2B","UCHL1","TUBB3","SNAP25","PLP1"),group.by = "ident", x.lab.rot = T,plot.legend = T)
```

```{r}
df_Hs<-FetchData(Hs_GA2123_Trachea_v3,c("ANO1","CFTR","SERPINB3","MUC16","specific_type"))

```

```{r, fig.height=3, fig.width=10}
ggplot(df_Hs,aes(specific_type,CFTR))+geom_dotplot(binaxis="y",aes(fill=specific_type),binwidth=0.05,stackdir="center",position=position_dodge(0.8), dotsize=0.018)+ theme(axis.text.x = element_text(angle = 45,hjust=1))+ stat_summary(aes(color=specific_type),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))
```

```{r,fig.width=10,fig.height=6}
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = F,group.by="specific_type",pt.size = 0.3)+scale_color_manual(values=c('#e6194b' , '#808080','#3cb44b', '#ffe119', '#4363d8', '#911eb4', '#46f0f0', '#f032e6', '#bcf60c', '#008080', '#e6beff', '#9a6324', '#fabebe',  '#800000', '#aaffc3', '#808000','#ffd8b1', '#000075', '#f58231', '#000000','#fffac8'
))

```

##### For the purpose of visualization, we average within each specific cell type:

```{r}
Hs_GA2123_Trachea_v3<-SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "specific_type")
average_Hs_specific_Annotation<-AverageExpression(object = Hs_GA2123_Trachea_v3,return.seurat = T)
```

```{r,fig.height=15,fig.width=15}

DoHeatmap(object = average_Hs_specific_Annotation, genes.use = c("EPCAM","TP63","KRT5","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","MUC16","SERPINB3","SOX9","KRT14","SOSTDC1","MUC5B","MUC5AC","SPDEF","LTF","LYZ","ACTA2","MYH11","POU5F1","ESRG","SNAP25","ASCL1","CHGA","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","RGS5","NOTCH3","COL2A1","ACAN","WNT2","PI16","CD34","THY1","TWIST2","MKI67","CFTR","ANO1"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.cex = 30,cex.row=20,group.order = c("Basal_SE","Basal_SMG","Ciliated_Foxn4","Ciliated","Secretory_SE",
 "Secretory_SMG", "Myoepithelial","Epcam_ECM","Stem","Schwann/Neural","Immune","LymphaticEndothelial","VascularEndothelial","Muscle","Chondrocyte","MesenchymalProgenitor","Fibroblast","CyclingFibroblast"))
```
```{r,fig.height=15,fig.width=15}

DoHeatmap(object = average_Hs_specific_Annotation, genes.use = c("EPCAM","TP63","KRT5","KRT14","SOSTDC1","SOX9","FOXN4","SHISA8","SNTN","FOXJ1","TFF3","MUC5B","SPDEF","LTF","LYZ","ACTA2","POU5F1","ESRG","TUBB3","SNAP25","CHGA","ASCL1","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","MYH11","RGS5","NOTCH3","COL2A1","ACAN","WNT2","CD34","THY1","CLEC3B","TWIST2","MKI67","CFTR","ANO1"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.cex = 30,cex.row=20,group.order = c("Basal_SE","Basal_SMG","Ciliated_Foxn4","Ciliated","Secretory_SE",
 "Secretory_SMG", "Myoepithelial","Epcam_ECM","Stem","Schwann/Neural","Immune","LymphaticEndothelial","VascularEndothelial","Muscle","Chondrocyte","MesenchymalProgenitor","Fibroblast","CyclingFibroblast"))
```

```{r}
save(Hs_GA2123_Trachea_v3_sub1,file="seurat_GA2123wk_v3_sub1.RData")
```
```{r}
save(Hs_GA2123_Trachea_v3,file="seurat_GA2123wk_v3.RData")
```
```{r}
#load(file="seurat_GA2123wk_v3.RData")
```

##### check a few virus receptors:

```{r,fig.height=20, fig.width=20}
VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("CDHR3","ACE2","TMPRSS2","TMPRSS4","ANPEP","DPP4","ST6GAL1","ST3GAL4"), nCol = 2,group.by="specific_type",point.size.use=0.3,x.lab.rot = T)
```































